From cda8274a740d0f7903a69113ae9c870a28955f66 Mon Sep 17 00:00:00 2001
From: ArthurSrZ <55806298+ArthurSrz@users.noreply.github.com>
Date: Thu, 5 Oct 2023 10:13:46 +0000
Subject: [PATCH] Pending changes exported from your codespace

---
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 .../README.md                                 |    0
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 1-Introduction/02-ethics/README.md            |  263 ----
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 .../02-ethics/translations/README.hi.md       |  259 ----
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 .../02-ethics/translations/README.ru.md       |  273 ----
 .../02-ethics/translations/assignment.hi.md   |   19 -
 .../02-ethics/translations/assignment.ko.md   |   21 -
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 .../04-stats-and-probability/notebook.ipynb   | 1122 -----------------
 .../solution/assignment.ipynb                 |  945 --------------
 47 files changed, 168 insertions(+), 4190 deletions(-)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/README.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/assignment.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/README.es.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/README.hi.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/README.ko.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/README.pt-br.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/README.ru.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/README.tr.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/assignment.hi.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/assignment.ko.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/assignment.pt-br.md (100%)
 rename 1-Introduction/{03-defining-data => 02-defining-data}/translations/assignment.ru.md (100%)
 delete mode 100644 1-Introduction/02-ethics/README.md
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 delete mode 100644 1-Introduction/02-ethics/translations/assignment.ru.md
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/README.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/assignment.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/images/boxplot_byrole.png (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/images/boxplot_explanation.png (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/images/height-boxplot.png (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/images/normal-histogram.png (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/images/probability-density.png (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/images/video-prob-and-stats.png (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/images/weight-boxplot.png (100%)
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 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/images/weight-histogram.png (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/translations/README.hi.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/translations/README.ko.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/translations/README.pt-br.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/translations/README.ru.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/translations/assignment.hi.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/translations/assignment.ko.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/translations/assignment.pt-br.md (100%)
 rename 1-Introduction/{04-stats-and-probability => 03-stats-and-probability}/translations/assignment.ru.md (100%)
 delete mode 100644 1-Introduction/04-stats-and-probability/assignment.ipynb
 delete mode 100644 1-Introduction/04-stats-and-probability/notebook.ipynb
 delete mode 100644 1-Introduction/04-stats-and-probability/solution/assignment.ipynb

diff --git a/1-Introduction/01-defining-data-science/notebook.ipynb b/1-Introduction/01-defining-data-science/notebook.ipynb
index cf3988e8..c7740cb8 100644
--- a/1-Introduction/01-defining-data-science/notebook.ipynb
+++ b/1-Introduction/01-defining-data-science/notebook.ipynb
@@ -2,55 +2,50 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# Challenge: Analyzing Text about Data Science\r\n",
-    "\r\n",
-    "In this example, let's do a simple exercise that covers all steps of a traditional data science process. You do not have to write any code, you can just click on the cells below to execute them and observe the result. As a challenge, you are encouraged to try this code out with different data. \r\n",
-    "\r\n",
-    "## Goal\r\n",
-    "\r\n",
-    "In this lesson, we have been discussing different concepts related to Data Science. Let's try to discover more related concepts by doing some **text mining**. We will start with a text about Data Science, extract keywords from it, and then try to visualize the result.\r\n",
-    "\r\n",
+    "# Challenge: Analyzing Text about Data Science\n",
+    "\n",
+    "In this example, let's do a simple exercise that covers all steps of a traditional data science process. You do not have to write any code, you can just click on the cells below to execute them and observe the result. As a challenge, you are encouraged to try this code out with different data. \n",
+    "\n",
+    "## Goal\n",
+    "\n",
+    "In this lesson, we have been discussing different concepts related to Data Science. Let's try to discover more related concepts by doing some **text mining**. We will start with a text about Data Science, extract keywords from it, and then try to visualize the result.\n",
+    "\n",
     "As a text, I will use the page on Data Science from Wikipedia:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
+   "metadata": {},
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "url = 'https://en.wikipedia.org/wiki/Data_science'"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "## Step 1: Getting the Data\r\n",
-    "\r\n",
+    "## Step 1: Getting the Data\n",
+    "\n",
     "First step in every data science process is getting the data. We will use `requests` library to do that:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 63,
-   "source": [
-    "import requests\r\n",
-    "\r\n",
-    "text = requests.get(url).content.decode('utf-8')\r\n",
-    "print(text[:1000])"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "<!DOCTYPE html>\n",
       "<html class=\"client-nojs\" lang=\"en\" dir=\"ltr\">\n",
@@ -61,77 +56,79 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "import requests\n",
+    "\n",
+    "text = requests.get(url).content.decode('utf-8')\n",
+    "print(text[:1000])"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "## Step 2: Transforming the Data\r\n",
-    "\r\n",
-    "The next step is to convert the data into the form suitable for processing. In our case, we have downloaded HTML source code from the page, and we need to convert it into plain text.\r\n",
-    "\r\n",
+    "## Step 2: Transforming the Data\n",
+    "\n",
+    "The next step is to convert the data into the form suitable for processing. In our case, we have downloaded HTML source code from the page, and we need to convert it into plain text.\n",
+    "\n",
     "There are many ways this can be done. We will use the simplest built-in [HTMLParser](https://docs.python.org/3/library/html.parser.html) object from Python. We need to subclass the `HTMLParser` class and define the code that will collect all text inside HTML tags, except `<script>` and `<style>` tags."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 64,
-   "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       " Data science - Wikipedia Data science From Wikipedia, the free encyclopedia Jump to navigation Jump to search Interdisciplinary field of study focused on deriving knowledge and insights from data Not to be confused with  information science . The existence of  Comet NEOWISE  (here depicted as a series of red dots) was discovered by analyzing  astronomical survey  data acquired by a  space telescope , the  Wide-field Infrared Survey Explorer . Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning ( classification  •  regression ) Decision trees Ensembles Bagging Boosting Random forest k -NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine \n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from html.parser import HTMLParser\n",
+    "\n",
+    "class MyHTMLParser(HTMLParser):\n",
+    "    script = False\n",
+    "    res = \"\"\n",
+    "    def handle_starttag(self, tag, attrs):\n",
+    "        if tag.lower() in [\"script\",\"style\"]:\n",
+    "            self.script = True\n",
+    "    def handle_endtag(self, tag):\n",
+    "        if tag.lower() in [\"script\",\"style\"]:\n",
+    "            self.script = False\n",
+    "    def handle_data(self, data):\n",
+    "        if str.strip(data)==\"\" or self.script:\n",
+    "            return\n",
+    "        self.res += ' '+data.replace('[ edit ]','')\n",
+    "\n",
+    "parser = MyHTMLParser()\n",
+    "parser.feed(text)\n",
+    "text = parser.res\n",
+    "print(text[:1000])"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "## Step 3: Getting Insights\r\n",
-    "\r\n",
-    "The most important step is to turn our data into some form from which we can draw insights. In our case, we want to extract keywords from the text, and see which keywords are more meaningful.\r\n",
-    "\r\n",
+    "## Step 3: Getting Insights\n",
+    "\n",
+    "The most important step is to turn our data into some form from which we can draw insights. In our case, we want to extract keywords from the text, and see which keywords are more meaningful.\n",
+    "\n",
     "We will use Python library called [RAKE](https://github.com/aneesha/RAKE) for keyword extraction. First, let's install this library in case it is not present: "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 65,
-   "source": [
-    "import sys\r\n",
-    "!{sys.executable} -m pip install nlp_rake"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Requirement already satisfied: nlp_rake in c:\\winapp\\miniconda3\\lib\\site-packages (0.0.2)\n",
       "Requirement already satisfied: numpy>=1.14.4 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (1.19.5)\n",
@@ -142,8 +139,8 @@
      ]
     },
     {
-     "output_type": "stream",
      "name": "stderr",
+     "output_type": "stream",
      "text": [
       "C:\\winapp\\Miniconda3\\lib\\site-packages\\secretstorage\\dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
       "  from cryptography.utils import int_from_bytes\n",
@@ -157,27 +154,24 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "import sys\n",
+    "!{sys.executable} -m pip install nlp_rake"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "The main functionality is available from `Rake` object, which we can customize using some parameters. In our case, we will set the minimum length of a keyword to 5 characters, minimum frequency of a keyword in the document to 3, and maximum number of words in a keyword - to 2. Feel free to play around with other values and observe the result."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
-   "source": [
-    "import nlp_rake\r\n",
-    "extractor = nlp_rake.Rake(max_words=2,min_freq=3,min_chars=5)\r\n",
-    "res = extractor.apply(text)\r\n",
-    "res"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[('machine learning', 4.0),\n",
@@ -202,217 +196,223 @@
        " ('forbes', 1.0)]"
       ]
      },
+     "execution_count": 66,
      "metadata": {},
-     "execution_count": 66
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "import nlp_rake\n",
+    "extractor = nlp_rake.Rake(max_words=2,min_freq=3,min_chars=5)\n",
+    "res = extractor.apply(text)\n",
+    "res"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "\r\n",
-    "We obtained a list terms together with associated degree of importance. As you can see, the most relevant disciplines, such as machine learning and big data, are present in the list at top positions.\r\n",
-    "\r\n",
-    "## Step 4: Visualizing the Result\r\n",
-    "\r\n",
+    "\n",
+    "We obtained a list terms together with associated degree of importance. As you can see, the most relevant disciplines, such as machine learning and big data, are present in the list at top positions.\n",
+    "\n",
+    "## Step 4: Visualizing the Result\n",
+    "\n",
     "People can interpret the data best in the visual form. Thus it often makes sense to visualize the data in order to draw some insights. We can use `matplotlib` library in Python to plot simple distribution of the keywords with their relevance:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 67,
-   "source": [
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "def plot(pair_list):\r\n",
-    "    k,v = zip(*pair_list)\r\n",
-    "    plt.bar(range(len(k)),v)\r\n",
-    "    plt.xticks(range(len(k)),k,rotation='vertical')\r\n",
-    "    plt.show()\r\n",
-    "\r\n",
-    "plot(res)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot(pair_list):\n",
+    "    k,v = zip(*pair_list)\n",
+    "    plt.bar(range(len(k)),v)\n",
+    "    plt.xticks(range(len(k)),k,rotation='vertical')\n",
+    "    plt.show()\n",
+    "\n",
+    "plot(res)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "There is, however, even better way to visualize word frequencies - using **Word Cloud**. We will need to install another library to plot the word cloud from our keyword list."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "!{sys.executable} -m pip install wordcloud"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "`WordCloud` object is responsible for taking in either original text, or pre-computed list of words with their frequencies, and returns and image, which can then be displayed using `matplotlib`:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 69,
-   "source": [
-    "from wordcloud import WordCloud\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "wc = WordCloud(background_color='white',width=800,height=600)\r\n",
-    "plt.figure(figsize=(15,7))\r\n",
-    "plt.imshow(wc.generate_from_frequencies({ k:v for k,v in res }))"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "<matplotlib.image.AxesImage at 0x224b8677400>"
       ]
      },
+     "execution_count": 69,
      "metadata": {},
-     "execution_count": 69
+     "output_type": "execute_result"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1080x504 with 1 Axes>"
-      ],
-      "image/png": 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"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "from wordcloud import WordCloud\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "wc = WordCloud(background_color='white',width=800,height=600)\n",
+    "plt.figure(figsize=(15,7))\n",
+    "plt.imshow(wc.generate_from_frequencies({ k:v for k,v in res }))"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We can also pass in the original text to `WordCloud` - let's see if we are able to get similar result:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 70,
-   "source": [
-    "plt.figure(figsize=(15,7))\r\n",
-    "plt.imshow(wc.generate(text))"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "<matplotlib.image.AxesImage at 0x224b9e5fbb0>"
       ]
      },
+     "execution_count": 70,
      "metadata": {},
-     "execution_count": 70
+     "output_type": "execute_result"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1080x504 with 1 Axes>"
-      ],
-      "image/png": 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"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "plt.figure(figsize=(15,7))\n",
+    "plt.imshow(wc.generate(text))"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 61,
-   "source": [
-    "wc.generate(text).to_file('images/ds_wordcloud.png')"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "<wordcloud.wordcloud.WordCloud at 0x224b99d76a0>"
       ]
      },
+     "execution_count": 61,
      "metadata": {},
-     "execution_count": 61
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "wc.generate(text).to_file('images/ds_wordcloud.png')"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "You can see that word cloud now looks more impressive, but it also contains a lot of noise (eg. unrelated words such as `Retrieved on`). Also, we get fewer keywords that consist of two words, such as *data scientist*, or *computer science*. This is because RAKE algorithm does much better job at selecting good keywords from text. This example illustrates the importance of data pre-processing and cleaning, because clear picture at the end will allow us to make better decisions.\r\n",
-    "\r\n",
-    "In this exercise we have gone through a simple process of extracting some meaning from Wikipedia text, in the form of keywords and word cloud. This example is quite simple, but it demonstrates well all typical steps a data scientist will take when working with data, starting from data acquisition, up to visualization.\r\n",
-    "\r\n",
+    "You can see that word cloud now looks more impressive, but it also contains a lot of noise (eg. unrelated words such as `Retrieved on`). Also, we get fewer keywords that consist of two words, such as *data scientist*, or *computer science*. This is because RAKE algorithm does much better job at selecting good keywords from text. This example illustrates the importance of data pre-processing and cleaning, because clear picture at the end will allow us to make better decisions.\n",
+    "\n",
+    "In this exercise we have gone through a simple process of extracting some meaning from Wikipedia text, in the form of keywords and word cloud. This example is quite simple, but it demonstrates well all typical steps a data scientist will take when working with data, starting from data acquisition, up to visualization.\n",
+    "\n",
     "In our course we will discuss all those steps in detail. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
+   "metadata": {},
+   "source": []
   },
   {
    "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
-  "orig_nbformat": 4,
+  "interpreter": {
+   "hash": "c28e7b6bf4e5b397b8288a85bf0a94ea8d3585ce2b01919feb195678ec71581b"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.11 64-bit ('base': conda)",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python",
-   "version": "3.8.11",
-   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
-   "pygments_lexer": "ipython3",
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
    "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.11 64-bit ('base': conda)"
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
   },
-  "interpreter": {
-   "hash": "c28e7b6bf4e5b397b8288a85bf0a94ea8d3585ce2b01919feb195678ec71581b"
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
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-# Introduction to Data Ethics
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/02-Ethics.png)|
-|:---:|
-| Data Science Ethics - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
----
-
-We are all data citizens living in a datafied world.
-
-Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell their data through online [Marketplaces and Exchanges](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/). As **App Developers**, we'll find it easier and cheaper to integrate data-driven insights and algorithm-driven automation into daily user experiences. But as AI becomes pervasive, we'll also need to understand the potential harms caused by the [weaponization](https://www.youtube.com/watch?v=TQHs8SA1qpk) of such algorithms at scale.
-
-Trends also indicate that we will create and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data by 2025. As **Data Scientists**, this gives us unprecedented levels of access to personal data. This means we can build behavioral profiles of users and influence decision-making in ways that create an [illusion of free choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) while potentially nudging users towards outcomes we prefer. It also raises broader questions on data privacy and user protections.
-
-Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI, and AI governance as key drivers for larger megatrends around _democratization_ and _industrialization_ of AI.
-
-![Gartner's Hype Cycle for AI - 2020](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
-
-In this lesson, we'll explore the fascinating area of data ethics - from core concepts and challenges, to case studies and applied AI concepts like governance - that help establish an ethics culture in teams and organizations that work with data and AI.
-
-
-
-
-## [Pre-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/2) 🎯
-
-## Basic Definitions
-
-Let's start by understanding the basic terminology.
-
-The word "ethics" comes from the [Greek word "ethikos"](https://en.wikipedia.org/wiki/Ethics) (and its root "ethos") meaning _character or moral nature_. 
-
-**Ethics** is about the shared values and moral principles that govern our behavior in society. Ethics is based not on laws but on
-widely accepted norms of what is "right vs. wrong". However, ethical considerations can influence corporate governance initiatives and government regulations that create more incentives for compliance.
-
-**Data Ethics** is a [new branch of ethics](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) that "studies and evaluates moral problems related to _data, algorithms and corresponding practices_". Here, **"data"** focuses on actions related to generation, recording, curation, processing dissemination, sharing ,and usage, **"algorithms"** focuses on AI, agents, machine learning ,and robots, and **"practices"** focuses on topics like responsible innovation, programming, hacking and ethics codes.
-
-**Applied Ethics** is the [practical application of moral considerations](https://en.wikipedia.org/wiki/Applied_ethics). It's the process of actively investigating ethical issues in the context of _real-world actions, products and processes_, and taking corrective measures to make that these remain aligned with our defined ethical values.
-
-**Ethics Culture** is about [_operationalizing_ applied ethics](https://hbr.org/2019/05/how-to-design-an-ethical-organization) to make sure that our ethical principles and practices are adopted in a consistent and scalable manner across the entire organization. Successful ethics cultures define organization-wide ethical principles, provide meaningful incentives for compliance, and reinforce ethics norms by encouraging and amplifying desired behaviors at every level of the organization.
-
-
-## Ethics Concepts
-
-In this section, we'll discuss concepts like **shared values** (principles) and **ethical challenges** (problems) for data ethics - and explore **case studies** that help you understand these concepts in real-world contexts.
-
-### 1. Ethics Principles
-
-Every data ethics strategy begins by defining _ethical principles_ - the "shared values" that describe acceptable behaviors, and guide compliant actions, in our data & AI projects. You can define these at an individual or team level. However, most large organizations outline these in an _ethical AI_ mission statement or framework that is defined at corporate levels and enforced consistently across all teams.
-
-**Example:** Microsoft's [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai) mission statement reads: _"We are committed to the advancement of AI-driven by ethical principles that put people first"_ - identifying 6 ethical principles in the framework below:
-
-![Responsible AI at Microsoft](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png)
-
-Let's briefly explore these principles. _Transparency_ and _accountability_ are foundational values that other principles built upon - so let's begin there:
-
-* [**Accountability**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) makes practitioners _responsible_ for their data & AI operations, and compliance with these ethical principles.
-* [**Transparency**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) ensures that data and AI actions are _understandable_ (interpretable) to users, explaining the what and why behind decisions.
-* [**Fairness**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) - focuses on ensuring AI treats _all people_ fairly, addressing any systemic or implicit socio-technical biases in data and systems.
-* [**Reliability & Safety**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - ensures that AI behaves _consistently_ with defined values, minimizing potential harms or unintended consequences.
-* [**Privacy & Security**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - is about understanding data lineage, and providing _data privacy and related protections_ to users.
-* [**Inclusiveness**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - is about designing AI solutions with intention, adapting them to meet a _broad range of human needs_ & capabilities.
-
-> 🚨 Think about what your data ethics mission statement could be. Explore ethical AI frameworks from other organizations - here are examples from [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles) ,and [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/). What shared values do they have in common? How do these principles relate to the AI product or industry they operate in?
-
-### 2. Ethics Challenges
-
-Once we have ethical principles defined, the next step is to evaluate our data and AI actions to see if they align with those shared values. Think about your actions in two categories: _data collection_ and _algorithm design_. 
-
-With data collection, actions will likely involve **personal data** or personally identifiable information (PII) for identifiable living individuals. This includes [diverse items of non-personal data](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en) that _collectively_ identify an individual. Ethical challenges can relate to _data privacy_, _data ownership_, and related topics like _informed consent_ and _intellectual property rights_ for users.
-
-With algorithm design, actions will involve collecting & curating **datasets**, then using them to train & deploy **data models** that predict outcomes or automate decisions in real-world contexts. Ethical challenges can arise from _dataset bias_, _data quality_ issues, _unfairness_ ,and _misrepresentation_ in algorithms - including some issues that are systemic in nature.
-
-In both cases, ethics challenges highlight areas where our actions may encounter conflict with our shared values. To detect, mitigate, minimize, or eliminate, these concerns - we need to ask moral "yes/no" questions related to our actions, then take corrective actions as needed. Let's take a look at some ethical challenges and the moral questions they raise:
-
-
-#### 2.1 Data Ownership
-
-Data collection often involves personal data that can identify the data subjects. [Data ownership](https://permission.io/blog/data-ownership) is about _control_ and [_user rights_](https://permission.io/blog/data-ownership) related to the creation, processing ,and dissemination of data. 
-
-The moral questions we need to ask are: 
- * Who owns the data? (user or organization)
- * What rights do data subjects have? (ex: access, erasure, portability)
- * What rights do organizations have? (ex: rectify malicious user reviews)
-
-#### 2.2 Informed Consent
-
-[Informed consent](https://legaldictionary.net/informed-consent/) defines the act of users agreeing to an action (like data collection) with a _full understanding_ of relevant facts including the purpose, potential risks, and alternatives. 
-
-Questions to explore here are:
- * Did the user (data subject) give permission for data capture and usage?
- * Did the user understand the purpose for which that data was captured?
- * Did the user understand the potential risks from  their participation?
-
-#### 2.3 Intellectual Property
-
-[Intellectual property](https://en.wikipedia.org/wiki/Intellectual_property) refers to intangible creations resulting from the human initiative, that may _have economic value_ to individuals or businesses. 
-
-Questions to explore here are:
- * Did the collected data have economic value to a user or business?
- * Does the **user** have intellectual property here?
- * Does the **organization** have intellectual property here?
- * If these rights exist, how are we protecting them?
-
-#### 2.4 Data Privacy
-
-[Data privacy](https://www.northeastern.edu/graduate/blog/what-is-data-privacy/) or information privacy refers to the preservation of user privacy and protection of user identity with respect to personally identifiable information. 
-
-Questions to explore here are:
- * Is users' (personal) data secured against hacks and leaks?
- * Is users' data accessible only to authorized users and contexts?
- * Is users' anonymity preserved when data is shared or disseminated?
- * Can a user be de-identified from anonymized datasets?
-
-
-#### 2.5 Right To Be Forgotten
-
-The [Right To Be Forgotten](https://en.wikipedia.org/wiki/Right_to_be_forgotten) or [Right to Erasure](https://www.gdpreu.org/right-to-be-forgotten/) provides additional personal data protection to users. Specifically, it gives users the right to request deletion or removal of personal data from Internet searches and other locations, _under specific circumstances_ - allowing them a fresh start online without past actions being held against them.
-
-Questions to explore here are:
- * Does the system allow data subjects to request erasure?
- * Should the withdrawal of user consent trigger automated erasure?
- * Was data collected without consent or by unlawful means?
- * Are we compliant with government regulations for data privacy?
-
-
-#### 2.6 Dataset Bias
-
-Dataset or [Collection Bias](http://researcharticles.com/index.php/bias-in-data-collection-in-research/) is about selecting a _non-representative_ subset of data for algorithm development, creating potential  unfairness in result outcomes for diverse groups. Types of bias include selection or sampling bias, volunteer bias, and instrument bias. 
-
-Questions to explore here are:
- * Did we recruit a representative set of data subjects?
- * Did we test our collected or curated dataset for various biases?
- * Can we mitigate or remove any discovered biases?
-
-#### 2.7 Data Quality
-
-[Data Quality](https://lakefs.io/data-quality-testing/) looks at the validity of the curated dataset used to develop our algorithms, checking to see if features and records meet requirements for the level of accuracy and consistency needed for our AI purpose.
-
-Questions to explore here are:
- * Did we capture valid _features_ for our use case?
- * Was data captured _consistently_ across diverse data sources?
- * Is the dataset _complete_ for diverse conditions or scenarios?
- * Is information captured _accurately_ in reflecting reality?
-
-#### 2.8 Algorithm Fairness
-
-[Algorithm Fairness](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) checks to see if the algorithm design systematically discriminates against specific subgroups of data subjects leading to [potential harms](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml) in _allocation_ (where resources are denied or withheld from that group) and _quality of service_ (where AI is not as accurate for some subgroups as it is for others). 
-
-Questions to explore here are:
- * Did we evaluate model accuracy for diverse subgroups and conditions?
- * Did we scrutinize the system for potential harms (e.g., stereotyping)?
- * Can we revise data or retrain models to mitigate identified harms?
-
-Explore resources like [AI Fairness checklists](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4t6dA) to learn more.
-
-#### 2.9 Misrepresentation
-
-[Data Misrepresentation](https://www.sciencedirect.com/topics/computer-science/misrepresentation) is about asking whether we are communicating insights from honestly reported data in a deceptive manner to support a desired narrative. 
-
-Questions to explore here are:
- * Are we reporting incomplete or inaccurate data?
- * Are we visualizing data in a manner that drives misleading conclusions?
- * Are we using selective statistical techniques to manipulate outcomes?
- * Are there alternative explanations that may offer a different conclusion?
-
-#### 2.10 Free Choice
-The [Illusion of Free Choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) occurs when system "choice architectures" use decision-making algorithms to nudge people towards taking a preferred outcome while seeming to give them options and control. These [dark patterns](https://www.darkpatterns.org/) can cause social and economic harm to users. Because user decisions impact behavior profiles, these actions potentially drive future choices that can amplify or extend the impact of these harms.
-
-Questions to explore here are:
- * Did the user understand the implications of making that choice?
- * Was the user aware of (alternative) choices and the pros & cons of each?
- * Can the user reverse an automated or influenced choice later?
-
-### 3. Case Studies
-
-To put these ethical challenges in real-world contexts, it helps to look at case studies that highlight the potential harms and consequences to individuals and society, when such ethics violations are overlooked. 
-
-Here are a few examples:
-
-| Ethics Challenge | Case Study  | 
-|--- |--- |
-| **Informed Consent** | 1972 - [Tuskegee Syphilis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - African American men who participated in the study were promised free medical care _but deceived_ by researchers who failed to inform subjects of their diagnosis or about availability of treatment. Many subjects died & partners or children were affected; the study lasted 40 years. | 
-| **Data Privacy** |  2007 - The [Netflix data prize](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) provided researchers with _10M anonymized movie rankings from 50K customers_ to help improve recommendation algorithms. However, researchers were able to correlate anonymized data with personally-identifiable data in _external datasets_ (e.g., IMDb comments) - effectively "de-anonymizing" some Netflix subscribers.|
-| **Collection Bias**  | 2013 - The City of Boston [developed Street Bump](https://www.boston.gov/transportation/street-bump), an app that let citizens report potholes, giving the city better roadway data to find and fix issues. However, [people in lower income groups had less access to cars and phones](https://hbr.org/2013/04/the-hidden-biases-in-big-data), making their roadway issues invisible in this app. Developers worked with academics to _equitable access and digital divides_ issues for fairness. |
-| **Algorithmic Fairness**  | 2018 - The MIT [Gender Shades Study](http://gendershades.org/overview.html) evaluated the accuracy of gender classification AI products, exposing gaps in accuracy for women and persons of color. A [2019 Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) seemed to offer less credit to women than men. Both illustrated issues in algorithmic bias leading to socio-economic harms.|
-| **Data Misrepresentation** | 2020 - The [Georgia Department of Public Health released COVID-19 charts](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening) that appeared to mislead citizens about trends in confirmed cases with non-chronological ordering on the x-axis. This illustrates misrepresentation through visualization tricks. |
-| **Illusion of free choice** | 2020 - Learning app [ABCmouse paid $10M to settle an FTC complaint](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) where parents were trapped into paying for subscriptions they couldn't cancel. This illustrates dark patterns in choice architectures, where users were nudged towards potentially harmful choices. |
-| **Data Privacy & User Rights** | 2021 - Facebook [Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) exposed data from 530M users, resulting in a $5B settlement to the FTC. It however refused to notify users of the breach violating user rights around data transparency and access. |
-
-Want to explore more case studies? Check out these resources:
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - ethics dilemmas across diverse industries. 
-* [Data Science Ethics course](https://www.coursera.org/learn/data-science-ethics#syllabus) - landmark case studies explored.
-* [Where things have gone wrong](https://deon.drivendata.org/examples/) - deon checklist with examples
-
-> 🚨 Think about the case studies you've seen - have you experienced, or been affected by, a similar ethical challenge in your life? Can you think of at least one other case study that illustrates one of the ethical challenges we've discussed in this section?
-
-## Applied Ethics
-
-We've talked about ethics concepts, challenges ,and case studies in real-world contexts. But how do we get started _applying_ ethical principles and practices in our projects? And how do we _operationalize_ these practices for better governance? Let's explore some real-world solutions: 
-
-### 1. Professional Codes
-
-Professional Codes offer one option for organizations to "incentivize" members to support their ethical principles and mission statement. Codes are _moral guidelines_ for professional behavior, helping employees or members make decisions that align with their organization's principles. They are only as good as the voluntary compliance from members; however, many organizations offer additional rewards and penalties to motivate compliance from members.
-
-Examples include:
-
- * [Oxford Munich](http://www.code-of-ethics.org/code-of-conduct/) Code of Ethics
- * [Data Science Association](http://datascienceassn.org/code-of-conduct.html) Code of Conduct (created 2013)
- * [ACM Code of Ethics and Professional Conduct](https://www.acm.org/code-of-ethics) (since 1993)
-
-> 🚨 Do you belong to a professional engineering or data science organization? Explore their site to see if they define a professional code of ethics. What does this say about their ethical principles? How are they "incentivizing" members to follow the code?
-
-### 2. Ethics Checklists
-
-While professional codes define required _ethical behavior_ from practitioners, they [have known limitations](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) in enforcement, particularly in large-scale projects. Instead, many data Science experts [advocate for checklists](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md), that can **connect principles to practices** in more deterministic and actionable ways. 
-
-Checklists convert questions into "yes/no" tasks that can be operationalized, allowing them to be tracked as part of standard product release workflows. 
-
-Examples include:
- * [Deon](https://deon.drivendata.org/) - a general-purpose data ethics checklist created from [industry recommendations](https://deon.drivendata.org/#checklist-citations) with a command-line tool for easy integration.
- * [Privacy Audit Checklist](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - provides general guidance for information handling practices from legal and social exposure perspectives.
- * [AI Fairness Checklist](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) - created by AI practitioners to support the adoption and integration of fairness checks into AI development cycles.
- * [22 questions for ethics in data and AI](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - more open-ended framework, structured for initial exploration of ethical issues in design, implementation, and organizational, contexts.
-
-### 3. Ethics Regulations
-
-Ethics is about defining shared values and doing the right thing _voluntarily_. **Compliance** is about _following the law_ if and where defined. **Governance** broadly covers all the ways in which organizations operate to enforce ethical principles and comply with established laws.
-
-Today, governance takes two forms within organizations. First, it's about defining **ethical AI** principles and establishing practices to operationalize adoption across all AI-related projects in the organization. Second, it's about complying with all government-mandated **data protection regulations** for regions it operates in.
-
-Examples of data protection and privacy regulations:
-
- * `1974`, [US Privacy Act](https://www.justice.gov/opcl/privacy-act-1974) - regulates _federal govt._ collection, use ,and disclosure of personal information.
- * `1996`, [US Health Insurance Portability & Accountability Act (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html) - protects personal health data.
- * `1998`, [US Children's Online Privacy Protection Act (COPPA)](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule) - protects data privacy of children under 13.
- * `2018`, [General Data Protection Regulation (GDPR)](https://gdpr-info.eu/) - provides user rights, data protection ,and privacy.
- * `2018`, [California Consumer Privacy Act (CCPA)](https://www.oag.ca.gov/privacy/ccpa) gives consumers more _rights_ over their (personal) data.
- * `2021`, China's [Personal Information Protection Law](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/) just passed, creating one of the strongest online data privacy regulations worldwide.
-
-> 🚨 The European Union defined GDPR (General Data Protection Regulation) remains one of the most influential data privacy regulations today. Did you know it also defines [8 user rights](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr) to protect citizens' digital privacy and personal data? Learn about what these are, and why they matter.
-
-
-### 4. Ethics Culture
-
-Note that there remains an intangible gap between _compliance_ (doing enough to meet "the letter of the law") and addressing [systemic issues](https://www.coursera.org/learn/data-science-ethics/home/week/4) (like ossification, information asymmetry, and distributional unfairness) that can speed up the weaponization of AI. 
-
-The latter requires [collaborative approaches to defining ethics cultures](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) that build emotional connections and consistent shared values _across organizations_ in the industry. This calls for more [formalized data ethics cultures](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) in organizations - allowing _anyone_ to [pull the Andon cord](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (to raise ethics concerns early in the process) and making _ethical assessments_ (e.g., in hiring) a core criteria team formation in AI projects.
-
----
-## [Post-lecture quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/3) 🎯
-## Review & Self Study 
-
-Courses and books help with understanding core ethics concepts and challenges, while case studies and tools help with applied ethics practices in real-world contexts. Here are a few resources to start with.
-
-* [Machine Learning For Beginners](https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md) - lesson on Fairness, from Microsoft.
-* [Principles of Responsible AI](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/) - free learning path from Microsoft Learn.
-* [Ethics and Data Science](https://resources.oreilly.com/examples/0636920203964) - O'Reilly EBook (M. Loukides, H. Mason et. al)
-* [Data Science Ethics](https://www.coursera.org/learn/data-science-ethics#syllabus) - online course from the University of Michigan.
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - case studies from the University of Texas.
-
-# Assignment 
-
-[Write A Data Ethics Case Study](assignment.md)
diff --git a/1-Introduction/02-ethics/assignment.md b/1-Introduction/02-ethics/assignment.md
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-## Write A Data Ethics Case Study
-
-## Instructions
-
-You've learned about various [Data Ethics Challenges](README.md#2-ethics-challenges) and seen some examples of [Case Studies](README.md#3-case-studies) reflecting data ethics challenges in real-world contexts.
-
-In this assignment, you'll write your own case study reflecting a data ethics challenge from your own experience, or from a relevant real-world context you are familiar with. Just follow these steps:
-
-1. `Pick a Data Ethics Challenge`. Look at [the lesson examples](README.md#2-ethics-challenges) or explore online examples like [the Deon Checklist](https://deon.drivendata.org/examples/) to get inspiration.
-
-2. `Describe a Real World Example`. Think about a situation you have heard of (headlines, research study etc.) or experienced (local community), where this specific challenge occurred. Think about the data ethics questions related to the challenge - and discuss the potential harms or unintended consequences that arise because of this issue. Bonus points: think about potential solutions or processes that may be applied here to help eliminate or mitigate the adverse impact of this challenge.
-
-3. `Provide a Related Resources list`. Share one or more resources (links to an article, a personal blog post or image, online research paper etc.) to prove this was a real-world occurrence. Bonus points: share resources that also showcase the potential harms & consequences from the incident, or highlight positive steps taken to prevent its recurrence.
-
-
-
-## Rubric
-
-Exemplary | Adequate | Needs Improvement
---- | --- | -- |
-One or more data ethics challenges are identified. <br/> <br/> The case study clearly describes a real-world incident reflecting that challenge, and highlights undesirable consequences or harms it caused. <br/><br/> There is at least one linked resource to prove this occurred. | One data ethics challenge is identified. <br/><br/> At least one relevant harm or consequence is discussed briefly. <br/><br/> However discussion is limited or lacks proof of real-world occurence. | A data challenge is identified. <br/><br/> However the description or resources do not adequately reflect the challenge or prove it's real-world occurence. |
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-# डेटा नैतिकता का परिचय
-
-|![[(@sketchthedocs) द्वारा स्केचनोट](https://sketchthedocs.dev) ](../../../sketchnotes/02-Ethics.png)|
-|:---:|
-| डेटा विज्ञान नैतिकता - _[@nitya](https://twitter.com/nitya) द्वारा स्केचनोट_ |
-
----
-
-हम सब इस डाटा-फाइड दुनिया में रहने वाले डाटा-नागरिक है | 
-
-बाजार के रुझान यह दर्शाते हैं कि २०२२ तक, तीन में से एक बड़ी संस्था अपना डाटा कि खरीद और बेचना ऑनलाइन [दुकानों](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/) द्वारा करेंगी | **ऐप डेवलपर** के रूप में, हम डेटा-संचालित अंतर्दृष्टि और एल्गोरिथम-चालित स्वचालन को दैनिक उपयोगकर्ता अनुभवों में एकीकृत करना आसान और सस्ता पाएंगे। लेकिन जैसे-जैसे AI व्यापक होता जाएगा, हमें इस तरह के एल्गोरिदम के [हथियारीकरण](https://www.youtube.com/watch?v=TQHs8SA1qpk) से होने वाले संभावित नुकसान को भी समझना होगा ।
-
-रुझान यह भी संकेत देते हैं कि हम २०२५ तक [180 ज़ेटाबाइट्स](https://www.statista.com/statistics/871513/worldwide-data-created/) डेटा का निर्माण और उपभोग करेंगे । **डेटा वैज्ञानिक** के रूप में, यह हमें व्यक्तिगत डेटा तक पहुंचने के लिये अभूतपूर्व स्तर प्रदान करता है । इसका मतलब है कि हम उपयोगकर्ताओं के व्यवहार संबंधी प्रोफाइल बना सकते हैं और निर्णय लेने को इस तरह से प्रभावित कर सकते हैं जो संभावित रूप से एक [मुक्त इच्छा का भ्रम](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) पैदा करता है जब्कि वह उपयोगकर्ताओं को हमारे द्वारा पसंद किए जाने वाले परिणामों की ओर आकर्षित करना । यह डेटा गोपनीयता और उपयोगकर्ता की सुरक्षा पर भी व्यापक प्रश्न उठाता है ।
-
-डेटा नैतिकता अब डेटा विज्ञान और इंजीनियरिंग का  _आवश्यक रक्षक_ हैं, जिससे हमें अपने डेटा-संचालित कार्यों से संभावित नुकसान और अनपेक्षित परिणामों को नीचे रखने में मदद मिलती है । [AI के लिए गार्टनर हाइप साइकिल](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) डिजिटल नैतिकता में उचित रुझानों की पहचान करता है AI के _democratization_ और _industrialization_ के आसपास बड़े मेगाट्रेंड के लिए प्रमुख ड्राइवर के रूप में जिम्मेदार AI की ज़िम्मेदारी और AI शासन ।
-
-
-![AI के लिए गार्टनर का प्रचार चक्र - २०२०](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
-
-इस पाठ में, हम डेटा नैतिकता के आकर्षक क्षेत्र के बारे में सीखेंगे  - मूल अवधारणाओं और चुनौतियों से लेकर केस-स्टडी और शासन जैसी एप्लाइड AI अवधारणाओं तक - जो डेटा और AI के साथ काम करने वाली समूह और संगठनों में नैतिकता संस्कृति स्थापित करने में मदद करते हैं ।
-
-## [पाठ से पहले की प्रश्नोत्तरी](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/2) 🎯
-
-## मूल परिभाषाएं
-
-आइए बुनियादी शब्दावली को समझना शुरू करें ।
-
-"नैतिकता" [ग्रीक शब्द "एथिकोस"](https://en.wikipedia.org/wiki/Ethics) (और इसकी जड़ "एथोस") से आया है जिसका अर्थ _चरित्र या नैतिक प्रकृति_ होता है ।
-
-**नैतिकता** उन साझा मूल्यों और नैतिक सिद्धांतों के बारे में है जो समाज में हमारे व्यवहार को नियंत्रित करते हैं । नैतिकता कानूनों पर नहीं बल्कि "सही बनाम गलत" के व्यापक रूप से स्वीकृत मानदंड पर आधारित है । लेकिन , नैतिक विचार कॉर्पोरेट प्रशासन की पहल और अनुपालन के लिए अधिक प्रोत्साहन पैदा करने वाले सरकारी नियमों को प्रभावित कर सकते हैं ।
-
-**डेटा नैतिकता** एक [नैतिकता की नई शाखा](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) है जो "_डेटा, एल्गोरिदम और से संबंधित नैतिक समस्याओं का अध्ययन और मूल्यांकन करती है_" । यहां, **"डेटा"** - निर्माण, रिकॉर्डिंग, अवधि, प्रसंस्करण प्रसार, साझाकरण और उपयोग से संबंधित कार्यों पर केंद्रित है, **"एल्गोरिदम"** AI , एजेंटों, मशीन लर्निंग और रोबोटो पर केंद्रित है, और ** "अभ्यास"** जिम्मेदार नवाचार, प्रोग्रामिंग, हैकिंग और नैतिकता कोड जैसे विषयों पर केंद्रित है ।
-
-**एप्लाइड नैतिकता** [नैतिक विचारों का व्यावहारिक अनुप्रयोग](https://en.wikipedia.org/wiki/Applied_ethics) है । यह _वास्तविक दुनिया की कार्रवाइयों, उत्पादों और प्रक्रियाओं_ के संदर्भ में नैतिक मुद्दों की सक्रिय रूप से जांच करने और सुधारात्मक उपाय करने की प्रक्रिया है ताकि ये हमारे परिभाषित नैतिक मूल्यों के साथ संरेखित रहें ।
-
-**नैतिकता संस्कृति** यह सुनिश्चित करने के लिए [_operationalizing_ एप्लाइड नैतिकता](https://hbr.org/2019/05/how-to-design-an-ethical-organization) के बारे में है कि हमारे नैतिक सिद्धांतों और प्रथाओं को पूरे संगठन में एक सुसंगत और मापनीय तरीके से अपनाया जाए । सफल नैतिक संस्कृतियाँ संगठन-व्यापी नैतिक सिद्धांतों को परिभाषित करती हैं, अनुपालन के लिए सार्थक प्रोत्साहन प्रदान करती हैं, और संगठन के हर स्तर पर वांछित व्यवहारों को प्रोत्साहित और प्रवर्धित करके नैतिक मानदंडों को सुदृढ़ करती हैं ।
-
-
-## नैतिकता की अवधारणाएं
-
-इस खंड में, हम डेटा नैतिकता के लिए साझा मूल्यों (सिद्धांतों) और नैतिक चुनौतियों (समस्याओं) जैसी अवधारणाओं पर चर्चा करेंगे - और मामले के अध्ययन का पता लगाएंगे जो आपको वास्तविक दुनिया के संदर्भों में इन अवधारणाओं को समझने में मदद करते हैं ।
-
-### 1. नैतिक सिद्धांत
-
-प्रत्येक डेटा नैतिकता रणनीति _नैतिक सिद्धांतों_ को परिभाषित करके शुरू होती है - "साझा मूल्य" जो स्वीकार्य व्यवहारों का वर्णन करते हैं, और हमारे डेटा और AI परियोजनाओं में अनुपालन कार्यों का मार्गदर्शन करते हैं । लेकिन, अधिकांश बड़े संगठन इन्हें एक _नैतिक AI_ मिशन स्टेटमेंट या फ्रेमवर्क में रेखांकित करते हैं जो कॉर्पोरेट स्तर पर परिभाषित होता है और सभी टीमों में लगातार लागू होता है ।
-
-**उदाहरण:** माइक्रोसॉफ्ट की [जिम्मेदार एआई](https://www.microsoft.com/en-us/ai/responsible-ai) मिशन स्टेटमेंट कहती है : _"हम नैतिक सिद्धांतों द्वारा संचालित AI की उन्नति के लिए प्रतिबद्ध हैं जो लोगों को सबसे पहले रखते हैं |"_ - नीचे दिए गए ढांचे में 6 नैतिक सिद्धांतों की वार्ना की गयी है :
-
-![माइक्रोसॉफ्ट की जिम्मेदार एआई](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png)
-
-आइए संक्षेप में इन सिद्धांतों के बारे में सीखे | _पारदर्शिता_ और _जवाबदेही_ वह मूलभूत मूल्य हैं जिन पर अन्य सिद्धांतों का निर्माण किया गया है - तो चलिए वहां शुरु करते हैं :
-
-* [**जवाबदेही**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) उपयोगकर्ताओं को उनके डेटा और AI संचालन, और इन नैतिक सिद्धांतों के अनुपालन के लिए _जिम्मेदार_ बनाती है ।
-* [**पारदर्शिता**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) सुनिश्चित करती है कि डेटा और AI क्रियाएं उपयोगकर्ताओं के लिए _समझने योग्य_ (व्याख्या योग्य) हैं, यह बताते हुए कि निर्णयों के पीछे क्या और क्यों है ।
-* [**निष्पक्षता**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) - यह सुनिश्चित करने पर ध्यान केंद्रित करती है कि AI डेटा और सिस्टम में किसी भी प्रणालीगत या निहित सामाजिक-तकनीकी पूर्वाग्रहों को संबोधित करते हुए _सभी लोगों_ के साथ उचित व्यवहार करता है ।
-* [**विश्वसनीयता और अहनिकारकता**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - सुनिश्चित करती है कि AI- संभावित नुकसान या अनपेक्षित परिणामों को कम करते हुए परिभाषित मूल्यों के साथ _लगातार_ काम करता है ।
-* [**निजता एवं सुरक्षा**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - डेटा वंश को समझने, और उपयोगकर्ताओं को _डेटा गोपनीयता और संबंधित सुरक्षा_ प्रदान करने के बारे में है ।
-* [**समग्रता**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - AI समाधानों को इरादे से डिजाइन करना एवं उन्हें _मानवीय आवश्यकताओं की एक विस्तृत श्रृंखला_ और क्षमताओं को पूरा करने के लिए अनुकूलित करने के बारे में है ।
-
-> 🚨 अपने डेटा नैतिकता मिशन वक्तव्य के बारे में सोचें | अन्य संगठनों से नैतिक AI ढांचों का अन्वेषण करें - ये हैं कुछ उदाहरण [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles) ,एवं [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/) | इनके बीच क्या साझा मूल्य हैं? ये सिद्धांत उनके द्वारा संचालित एआई उत्पाद या उद्योग से कैसे संबंधित हैं ?
-
-### 2. नैतिकता से जुडी चुनौतियां
-
-एक बार जब हमारे पास नैतिक सिद्धांत परिभाषित हो जाते हैं, तो अगला कदम यह देखने के लिए हमारे डेटा और एआई कार्यों का मूल्यांकन करना है कि क्या वे उन साझा मूल्यों के साथ संरेखित हैं । अपने कार्यों के बारे में दो श्रेणियों में सोचें: _डेटा संग्रह_ और _एल्गोरिदम डिज़ाइन_ | 
-
-डेटा संग्रह के साथ, कार्रवाइयों में संभवतः पहचान योग्य जीवित व्यक्तियों के लिए **व्यक्तिगत डेटा** या व्यक्तिगत रूप से पहचान योग्य जानकारी शामिल होगी । इसमें [गैर-व्यक्तिगत डेटा के विविध आइटम](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en) शामिल हैं, जो _collectively_ किसी व्यक्ति की पहचान करते हैं । नैतिक चुनौतियां _डेटा गोपनीयता_, _डेटा स्वामित्व_, और उपयोगकर्ताओं के लिए _सूचित सहमति_ और _बौद्धिक संपदा अधिकार_ जैसे संबंधित विषयों से संबंधित हो सकती हैं ।
-
-एल्गोरिथम डिज़ाइन के साथ, क्रियाओं में **डेटासेट** एकत्र करना और क्यूरेट करना शामिल होगा, फिर उनका उपयोग **डेटा मॉडल** को प्रशिक्षित और तैनात करने के लिए किया जाएगा जो वास्तविक दुनिया के संदर्भों में परिणामों की भविष्यवाणी या स्वचालित निर्णय लेते हैं । एल्गोरिथम डिज़ाइन के साथ, क्रियाओं में **डेटासेट** एकत्र करना और क्यूरेट करना शामिल होगा, फिर उनका उपयोग **डेटा मॉडल** को प्रशिक्षित और तैनात करने के लिए किया जाएगा जो वास्तविक दुनिया के संदर्भों में परिणामों की भविष्यवाणी या स्वचालित निर्णय लेते हैं ।
-
-दोनों ही मामलों में, नैतिकता की चुनौतियाँ उन क्षेत्रों को उजागर करती हैं जहाँ हमारे कार्यों का हमारे साझा मूल्यों के साथ टकराव हो सकता है । इन चिंताओं का पता लगाने, सामना करने, कम करने या समाप्त करने के लिए - हमें अपने कार्यों से संबंधित नैतिक "हां या नहीं" प्रश्न पूछने की जरूरत है, फिर आवश्यकतानुसार सुधारात्मक कार्रवाई करें । आइए कुछ नैतिक चुनौतियों और उनके द्वारा उठाए गए नैतिक प्रश्नों पर एक नज़र डालें :
-
-
-#### 2.1 डेटा स्वामित्व
-
-डेटा संग्रह में अक्सर व्यक्तिगत डेटा शामिल होता है जो डेटा विषयों की पहचान कर सकता है । [डेटा स्वामित्व](https://permission.io/blog/data-ownership) _नियंत्रण_ के बारे में और उन [उपयोगकर्ता अधिकारो(https://permission.io/blog/data-ownership) के सम्भंदित है जो निर्माण , प्रसंस्करण और से संबंधित है । 
-
-हमें जो नैतिक प्रश्न पूछने चाहिए, वे हैं : 
- * डेटा का मालिक कौन है ? (उपयोगकर्ता या संगठन)
- * डेटा विषयों के पास क्या अधिकार हैं ? (उदा: पहुंच, मिटाना, सुवाह्यता)
- * संगठनों के पास क्या अधिकार हैं ? (उदा: दुर्भावनापूर्ण उपयोगकर्ता समीक्षाओं का सुधार)
-
-#### 2.2 सूचित सहमति
-
-[सूचित सहमति](https://legaldictionary.net/informed-consent/) उद्देश्य, संभावित जोखिमों और विकल्पों सहित प्रासंगिक तथ्यों की _पूर्ण समझ_ के साथ कार्रवाई (जैसे डेटा संग्रह) के लिए सहमत होने वाले उपयोगकर्ताओं के कार्य को परिभाषित करता है । 
-
-यहां देखने लायक प्रश्न हैं :
- * क्या उपयोगकर्ता (डेटा विषय) ने डेटा कैप्चर और उपयोग के लिए अनुमति दी थी ?
- * क्या उपयोगकर्ता को वह उद्देश्य समझ में आया जिसके लिए उस डेटा को कैप्चर किया गया था ?
- * क्या उपयोगकर्ता ने उनकी भागीदारी से संभावित जोखिमों को समझा ?
-
-#### 2.3 बौद्धिक संपदा
-
-[बौद्धिक संपदा](https://en.wikipedia.org/wiki/Intellectual_property) मानव पहल से उत्पन्न अमूर्त कृतियों को संदर्भित करता है, जिनका व्यक्तियों या व्यवसायों के लिए _आर्थिक_ महत्व हो सकता है ।
-
-यहां देखने लायक प्रश्न हैं :
- * क्या जमा किए गए डेटा का किसी उपयोगकर्ता या व्यवसाय के लिए आर्थिक महत्व है ?
- * क्या **उपयोगकर्ता** के पास यहां बौद्धिक संपदा है ?
- * क्या **संगठन** के पास यहां बौद्धिक संपदा है ?
- * अगर ये अधिकार मौजूद हैं, तो हम उनकी रक्षा कैसे कर रहे हैं ?
-
-#### 2.4 डाटा गोपनीयता
-
-[डेटा गोपनीयता](https://www.northeaster.edu/graduate/blog/what-is-data-privacy/) या सूचना गोपनीयता व्यक्तिगत रूप से पहचान योग्य जानकारी के संबंध में उपयोगकर्ता की गोपनीयता के संरक्षण और उपयोगकर्ता की पहचान की सुरक्षा को संदर्भित करता है ।
-
-यहां देखने लायक प्रश्न हैं :
- * क्या उपयोगकर्ताओं का (व्यक्तिगत) डेटा हैक और लीक से सुरक्षित है ?
- * क्या उपयोगकर्ताओं का डेटा केवल अधिकृत उपयोगकर्ताओं और संदर्भों के लिए सुलभ है ?
- * क्या डेटा साझा या प्रसारित होने पर उपयोगकर्ताओं की गोपनीयता बनी रहती है ?
- * क्या किसी उपयोगकर्ता की पहचान अज्ञात डेटासेट से की जा सकती है ?
-
-#### 2.5 भूला दिया जाने का अधिकार
-
-[भूला दिया जाने का अधिकार](https://en.wikipedia.org/wiki/Right_to_be_forgotten) अतिरिक्त सुविधाएं प्रदान करता है उपयोगकर्ताओं के लिए व्यक्तिगत डेटा सुरक्षा। विशेष रूप से, यह उपयोगकर्ताओं को इंटरनेट खोजों और अन्य स्थानों से व्यक्तिगत डेटा को हटाने या हटाने का अनुरोध करने का अधिकार देता है, _विशिष्ट परिस्थितियों में_ - उन्हें उनके खिलाफ पिछली कार्रवाई किए बिना ऑनलाइन एक नई शुरुआत करने की अनुमति देता है ।
-
-यहां देखने लायक प्रश्न हैं :
- * क्या सिस्टम डेटा विषयों को अपना डेटा मिटाने का अनुरोध करने की अनुमति देता है ?
- * क्या उपयोगकर्ता की सहमति वापस लेने से स्वचालित डेटा मिटाना शुरू हो जाएगा ?
- * क्या डेटा सहमति के बिना या गैरकानूनी तरीके से एकत्र किया गया था ?
- * क्या हम डेटा गोपनीयता के लिए सरकारी नियमों का अनुपालन करते हैं ?
-
-
-#### 2.6 डेटासेट पूर्वाग्रह
-
-डेटासेट या [संग्रह पूर्वाग्रह](http://researcharticles.com/index.php/bias-in-data-collection-in-research/) एल्गोरिथम विकास के लिए डेटा के _गैर-प्रतिनिधि_ सबसेट का चयन करने के बारे में है, जिसमें संभावित अनुचितता पैदा होती है विभिन्न समूहों के लिए भेदभाव । पूर्वाग्रह के प्रकारों में चयन या नमूना पूर्वाग्रह, स्वयंसेवी पूर्वाग्रह और साधन पूर्वाग्रह शामिल हैं ।
-
-यहां देखने लायक प्रश्न हैं :
- * क्या हमने डेटा विषयों के प्रतिनिधि सेट की भर्ती की ?
- * क्या हमने विभिन्न पूर्वाग्रहों के लिए अपने एकत्रित या क्यूरेट किए गए डेटासेट का परीक्षण किय ा?
- * क्या हम खोजे गए पूर्वाग्रहों को कम कर सकते हैं या हटा सकते हैं ?
-
-#### 2.7 डेटा की गुणवत्ता
-
-[डेटा गुणवत्ता](https://lakefs.io/data-quality-testing/) जो हमारे एल्गोरिदम को विकसित करने के लिए उपयोग किए गए क्यूरेट किए गए डेटासेट की वैधता को देखता है, यह देखने के लिए जाँच करता है कि सुविधाएँ और रिकॉर्ड सटीकता और स्थिरता के स्तर की आवश्यकताओं को पूरा करते हैं या नहीं हमारे AI उद्देश्य के लिए आवश्यक है ।
-
-यहां देखने लायक प्रश्न हैं :
- * क्या हमने अपने उपयोग के मामले में मान्य _features_ को कैप्चर किया ?
- * क्या डेटा विविध डेटा स्रोतों से _लगातार_ कैप्चर किया गया था ? 
- * क्या विविध स्थितियों या परिदृश्यों के लिए डेटासेट _पूर्ण_ है ?
- * क्या वास्तविकता को प्रतिबिंबित करने में जानकारी _सटीक_ रूप से कैप्चर की गई है ?
-
-#### 2.8 एल्गोरिथम की निष्पक्षता
-
-[एल्गोरिदम निष्पक्षता](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) यह देखने के लिए जांच करता है कि क्या एल्गोरिथम डिज़ाइन व्यवस्थित रूप से डेटा विषयों के विशिष्ट उपसमूहों के साथ भेदभाव करता है जिससे [संभावित नुकसान](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml) होते हैं  में _allocation_ (जहां संसाधनों को अस्वीकार कर दिया जाता है या उस समूह से रोक दिया जाता है) और _सेवा की गुणवत्ता_ (जहां AI कुछ उपसमूहों के लिए उतना सटीक नहीं है जितना कि यह दूसरों के लिए है) । 
-
-यहां देखने लायक प्रश्न हैं :
- * क्या हमने विविध उपसमूहों और स्थितियों के लिए मॉडल सटीकता का मूल्यांकन किया ?
- * क्या हमने संभावित नुकसान (जैसे, स्टीरियोटाइपिंग) के लिए सिस्टम की जांच की ?
- * क्या हम पहचाने गए नुकसान को कम करने के लिए डेटा को संशोधित कर सकते हैं या मॉडल को फिर से प्रशिक्षित कर सकते हैं ?
-
-अधिक जानने के लिए [AI फेयरनेस चेकलिस्ट](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4t6dA) जैसे संसाधनों का अन्वेषण करें ।
-
-#### 2.9 मिथ्या निरूपण
-
-[डेटा मिसरिप्रेजेंटेशन](https://www.sciencedirect.com/topics/computer-science/misrepresentation) यह पूछने के बारे में है कि क्या हम एक वांछित कथा का समर्थन करने के लिए भ्रामक तरीके से ईमानदारी से रिपोर्ट किए गए डेटा से अंतर्दृष्टि का संचार कर रहे हैं ।
-
-यहां देखने लायक प्रश्न हैं :
- * क्या हम अपूर्ण या गलत डेटा की रिपोर्ट कर रहे हैं ?
- * क्या हम डेटा को इस तरह से देख रहे हैं जिससे भ्रामक निष्कर्ष निकलते हैं ?
- * क्या हम परिणामों में हेरफेर करने के लिए चुनिंदा सांख्यिकीय तकनीकों का उपयोग कर रहे हैं ?
- * क्या ऐसे वैकल्पिक स्पष्टीकरण हैं जो एक अलग निष्कर्ष प्रस्तुत कर सकते हैं ?
-
-#### 2.10 मुक्त चयन
-[इल्यूज़न ऑफ़ फ्री चॉइस](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) तब होता है जब सिस्टम "चॉइस आर्किटेक्चर" लोगों को पसंदीदा परिणाम लेने के लिए प्रेरित करने के लिए निर्णय लेने वाले एल्गोरिदम का उपयोग करता है। जबकि उन्हें विकल्प और नियंत्रण देना प्रतीत होता है। ये [डार्क पैटर्न](https://www.darkpatterns.org/) उपयोगकर्ताओं को सामाजिक और आर्थिक नुकसान पहुंचा सकते हैं। चूंकि उपयोगकर्ता निर्णय व्यवहार प्रोफाइल को प्रभावित करते हैं, इसलिए ये कार्रवाइयां संभावित रूप से भविष्य के विकल्पों को प्रेरित करती हैं जो इन नुकसानों के प्रभाव को बढ़ा या बढ़ा सकते हैं।
-
-यहां देखने लायक प्रश्न हैं :
- * क्या उपयोगकर्ता ने उस विकल्प को बनाने के निहितार्थों को समझा ?
- * क्या उपयोगकर्ता (वैकल्पिक) विकल्पों और प्रत्येक के पेशेवरों और विपक्षों से अवगत था ?
- * क्या उपयोगकर्ता किसी स्वचालित या प्रभावित विकल्प को बाद में उलट सकता है ?
-
-### 3. केस स्टडी
-
-इन नैतिक चुनौतियों को वास्तविक दुनिया के संदर्भों में रखने के लिए, ऐसे मामलों के अध्ययन को देखने में मदद मिलती है जो व्यक्तियों और समाज को संभावित नुकसान और परिणामों को उजागर करते हैं, जब ऐसे नैतिकता उल्लंघनों की अनदेखी की जाती है ।
-
-कुछ उदाहरण निम्नलिखित हैं :
-
-| नैतिकता चुनौती | मामले का अध्ययन | 
-|--- |--- |
-| **सूचित सहमति** | १९७२ - [टस्केगी सिफलिस अध्ययन](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - अध्ययन में भाग लेने वाले अफ्रीकी अमेरिकी पुरुषों को उन शोधकर्ताओं द्वारा मुफ्त चिकित्सा देखभाल का वादा किया गया था जो उनके निदान या उपचार की उपलब्धता के बारे में विषयों को सूचित करने में विफल रहे। कई विषयों की मृत्यु हो गई और साथी या बच्चे प्रभावित हुए; अध्ययन 40 साल तक चला । | 
-| **डाटा प्राइवेसी** |  २००७ - [नेटफ्लिक्स डेटा प्राइज](https://www.wired.com/2007/12/why-anonymous-data-only-isnt/) ने शोधकर्ताओं को सिफारिश एल्गोरिदम को बेहतर बनाने में मदद करने के लिए 50K ग्राहकों_ से _10M अनाम मूवी रैंकिंग प्रदान की। हालांकि, शोधकर्ता अज्ञात डेटा को व्यक्तिगत रूप से पहचाने जाने योग्य डेटा के साथ _बाहरी डेटासेट_ (उदाहरण के लिए, IMDb टिप्पणियों) में सहसंबंधित करने में सक्षम थे - कुछ नेटफ्लिक्स ग्राहकों को प्रभावी रूप से "डी-अनामीकरण" ।|
-| **संग्रह पूर्वाग्रह**  | २०१३ - द सिटी ऑफ़ बोस्टन [विकसित स्ट्रीट बम्प](https://www.boston.gov/transportation/street-bump), एक ऐप जो नागरिकों को गड्ढों की रिपोर्ट करने देता है, जिससे शहर को समस्याओं को खोजने और ठीक करने के लिए बेहतर रोडवे डेटा मिलता है । हालांकि, [निम्न आय वर्ग के लोगों के पास कारों और फोन तक कम पहुंच थी](https://hbr.org/2013/04/the-hidden-biases-in-big-data), जिससे इस ऐप में उनके सड़क संबंधी मुद्दे अदृश्य हो गए थे। . डेवलपर्स ने शिक्षाविदों के साथ निष्पक्षता के लिए _न्यायसंगत पहुंच और डिजिटल विभाजन_ मुद्दों पर काम किया । |
-| **एल्गोरिथम निष्पक्षता**  | २०१८ - एमआईटी [जेंडर शेड्स स्टडी](http://gendershades.org/overview.html) ने लिंग वर्गीकरण एआई उत्पादों की सटीकता का मूल्यांकन किया, महिलाओं और रंग के व्यक्तियों के लिए सटीकता में अंतराल को उजागर किया । एक [2019 ऐप्पल कार्ड](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) पुरुषों की तुलना में महिलाओं को कम क्रेडिट प्रदान करता है। दोनों ने एल्गोरिथम पूर्वाग्रह में सचित्र मुद्दों को सामाजिक-आर्थिक नुकसान की ओर अग्रसर किया ।|
-| **डेटा गलत बयानी** | २०२० - [जॉर्जिया डिपार्टमेंट ऑफ पब्लिक हेल्थ ने जारी किया COVID-19 चार्ट](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening) जो एक्स-अक्ष पर गैर-कालानुक्रमिक क्रम के साथ पुष्टि किए गए मामलों में रुझानों के बारे में नागरिकों को गुमराह करने के लिए प्रकट हुए। यह विज़ुअलाइज़ेशन ट्रिक्स के माध्यम से गलत बयानी दिखाता है । |
-| **स्वतंत्र चुनाव का भ्रम** | २०२० - लर्निंग ऐप [एबीसीमाउस ने एफटीसी शिकायत को निपटाने के लिए 10 मिलियन डॉलर का भुगतान किया](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) जहां माता-पिता भुगतान करने में फंस गए थे सदस्यता वे रद्द नहीं कर सके । यह पसंद वास्तुकला में काले पैटर्न को दिखाता है, जहां उपयोगकर्ता संभावित रूप से हानिकारक विकल्पों की ओर झुकाव कर रहे थे । |
-| **डेटा गोपनीयता और उपयोगकर्ता अधिकार** | २०२१ - फेसबुक [डेटा ब्रीच](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) 530M उपयोगकर्ताओं के डेटा को उजागर किया, जिसके परिणामस्वरूप FTC को $ 5B का समझौता हुआ । हालांकि इसने डेटा पारदर्शिता और पहुंच के आसपास उपयोगकर्ता अधिकारों का उल्लंघन करने वाले उल्लंघन के उपयोगकर्ताओं को सूचित करने से इनकार कर दिया । |
-
-अधिक केस स्टडी के बारे में चाहते हैं ? इन संसाधनों की जाँच करें :
-* [नैतिकता अलिखित](https://ethicsunwrapped.utexas.edu/case-studies) - विविध उद्योगों में नैतिकता की दुविधा । 
-* [डेटा साइंस एथिक्स कोर्स](https://www.coursera.org/learn/data-science-ethics#syllabus) - ऐतिहासिक मामले का अध्ययन ।
-* [जहां चीजें गलत हो गई हैं](https://deon.drivendata.org/examples/) - उदाहरण के साथ डीओन चेकलिस्ट |
-
-> 🚨 आपके द्वारा देखी गई केस स्टडी के बारे में सोचें - क्या आपने अपने जीवन में इसी तरह की नैतिक चुनौती का अनुभव किया है, या इससे प्रभावित हुए हैं ? क्या आप कम से कम एक अन्य केस स्टडी के बारे में सोच सकते हैं जो इस खंड में चर्चा की गई नैतिक चुनौतियों में से एक को दर्शाती है ?
-
-## एप्लाइड नैतिकता
-
-हमने वास्तविक दुनिया के संदर्भों में नैतिक अवधारणाओं, चुनौतियों और केस स्टडी के बारे में बात की है। लेकिन हम अपनी परियोजनाओं में नैतिक सिद्धांतों और प्रथाओं को _लागू करना_ कैसे शुरू करते हैं ? और हम बेहतर शासन के लिए इन प्रथाओं को कैसे _संचालन_कृत करते हैं ? आइए कुछ वास्तविक दुनिया के समाधान देखें :
-
-### 1. व्यावसायिक कोड
-
-व्यावसायिक कोड संगठनों के लिए सदस्यों को उनके नैतिक सिद्धांतों और मिशन वक्तव्य का समर्थन करने के लिए "प्रोत्साहित" करने के लिए एक विकल्प प्रदान करते हैं । पेशेवर व्यवहार के लिए कोड _नैतिक दिशानिर्देश_ हैं, जो कर्मचारियों या सदस्यों को उनके संगठन के सिद्धांतों के अनुरूप निर्णय लेने में मदद करते हैं । वे केवल उतने ही अच्छे हैं जितने सदस्यों से स्वैच्छिक अनुपालन; हालांकि, कई संगठन सदस्यों से अनुपालन को प्रेरित करने के लिए अतिरिक्त पुरस्कार और दंड प्रदान करते हैं ।
-
-उदाहरणों में शामिल :
-
- * [ऑक्सफोर्ड म्यूनिख](http://www.code-of-ethics.org/code-of-conduct/) आचार संहिता
- * [डेटा साइंस एसोसिएशन](http://datascienceassn.org/code-of-conduct.html) आचार संहिता (2013 में बनाया गया)
- * [एसीएम आचार संहिता और व्यावसायिक आचरण](https://www.acm.org/code-of-ethics) (1993 से)
-
-> 🚨 क्या आप एक पेशेवर इंजीनियरिंग या डेटा विज्ञान संगठन से संबंधित हैं ? यह देखने के लिए कि क्या वे पेशेवर आचार संहिता को परिभाषित करते हैं, उनकी साइट का अन्वेषण करें । यह उनके नैतिक सिद्धांतों के बारे में क्या कहता है ? वे सदस्यों को कोड का पालन करने के लिए "प्रोत्साहित" कैसे कर रहे हैं ?
-
-### 2. नैतिकता जाँच सूची
-
-जबकि पेशेवर कोड चिकित्सकों से आवश्यक _नैतिक व्यवहार_ को परिभाषित करते हैं, वे प्रवर्तन में [विशेष रूप से बड़े पैमाने पर परियोजनाओं में](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) [ज्ञात सीमाएं हैं] । इसके बजाय, कई डेटा विज्ञान विशेषज्ञ [चेकलिस्ट के वकील](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md), जो **सिद्धांतों को अभ्यासों से जोड़ सकते हैं** अधिक नियतात्मक और कार्रवाई योग्य तरीके ।
-
-चेकलिस्ट प्रश्नों को "हां/नहीं" कार्यों में परिवर्तित करते हैं जिन्हें संचालित किया जा सकता है, जिससे उन्हें मानक उत्पाद रिलीज वर्कफ़्लो के हिस्से के रूप में ट्रैक किया जा सकता है ।
-
-उदाहरणों में शामिल :
- * [डियोन](https://deon.drivendata.org/) - आसान एकीकरण के लिए कमांड-लाइन टूल के साथ [उद्योग अनुशंसाओं](https://deon.drivedata.org/#checklist-citations) से बनाई गई एक सामान्य-उद्देश्य डेटा नैतिकता चेकलिस्ट ।
- * [गोपनीयता ऑडिट चेकलिस्ट](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - कानूनी और सामाजिक जोखिम के दृष्टिकोण से सूचना प्रबंधन प्रथाओं के लिए सामान्य मार्गदर्शन प्रदान करता है ।
- * [एआई फेयरनेस चेकलिस्ट](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) - एआई विकास चक्रों में निष्पक्षता जांच को अपनाने और एकीकरण का समर्थन करने के लिए एआई चिकित्सकों द्वारा बनाया गया ।
- * [डेटा और एआई में नैतिकता के लिए 22 प्रश्न](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - डिजाइन, कार्यान्वयन, और संगठनात्मक, संदर्भों में नैतिक मुद्दों की प्रारंभिक खोज के लिए संरचित, अधिक खुला ढांचा ।
- 
-### 3. नैतिकता विनियम
-
-नैतिकता साझा मूल्यों को परिभाषित करने और _स्वेच्छा_ से सही काम करने के बारे में है । **अनुपालन** _कानून का पालन करने के बारे में है_ यदि और जहां परिभाषित किया गया है । **शासन** मोटे तौर पर उन सभी तरीकों को शामिल करता है जिनमें संगठन नैतिक सिद्धांतों को लागू करने और स्थापित कानूनों का पालन करने के लिए काम करते हैं ।
-
-आज, संगठनों के भीतर शासन दो रूप लेता है । सबसे पहले, यह **नैतिक एआई** सिद्धांतों को परिभाषित करने और संगठन में सभी एआई-संबंधित परियोजनाओं में गोद लेने के संचालन के लिए प्रथाओं को स्थापित करने के बारे में है । दूसरा, यह उन क्षेत्रों के लिए सरकार द्वारा अनिवार्य सभी **डेटा सुरक्षा नियमों** का अनुपालन करने के बारे में है जहां यह संचालित होता है ।
-
-डेटा सुरक्षा और गोपनीयता नियमों के उदाहरण :
-
- * `१९७४`, [यूएस गोपनीयता अधिनियम](https://www.justice.gov/opcl/privacy-act-1974) - व्यक्तिगत जानकारी के संग्रह, उपयोग और प्रकटीकरण को नियंत्रित करता है ।
- * `१९९६`, [अमेरिकी स्वास्थ्य बीमा सुवाह्यता और जवाबदेही अधिनियम (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html) - व्यक्तिगत स्वास्थ्य डेटा की सुरक्षा करता है ।
- * `१९९८`, [यूएस चिल्ड्रन ऑनलाइन प्राइवेसी प्रोटेक्शन एक्ट (COPPA)](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule) - 13 साल से कम उम्र के बच्चों की डेटा गोपनीयता की रक्षा करता है ।
- * `२०१८`, [सामान्य डेटा संरक्षण विनियमन (GDPR)](https://gdpr-info.eu/) - उपयोगकर्ता अधिकार, डेटा सुरक्षा और गोपनीयता प्रदान करता है ।
- * `२०१८`, [कैलिफोर्निया उपभोक्ता गोपनीयता अधिनियम (CCPA)](https://www.oag.ca.gov/privacy/ccpa) उपभोक्ताओं को उनके (व्यक्तिगत) डेटा पर अधिक _अधिकार_ देता है ।
- * `२०२१`, चीन का [व्यक्तिगत सूचना संरक्षण कानून](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/) अभी-अभी पारित हुआ, दुनिया भर में सबसे मजबूत ऑनलाइन डेटा गोपनीयता नियमों में से एक बना ।
-
-> 🚨 यूरोपीय संघ परिभाषित GDPR (जनरल डेटा प्रोटेक्शन रेगुलेशन) आज सबसे प्रभावशाली डेटा गोपनीयता नियमों में से एक है । क्या आप जानते हैं कि यह नागरिकों की डिजिटल गोपनीयता और व्यक्तिगत डेटा की सुरक्षा के लिए [8 उपयोगकर्ता अधिकार](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr) को भी परिभाषित करता है ? जानें कि ये क्या हैं, और क्यों मायने रखते हैं ।
-
-
-### 4. नैतिकता संस्कृति
-
-ध्यान दें कि _अनुपालन_ ("कानून के पत्र को पूरा करने के लिए पर्याप्त प्रयास करना") और [प्रणालीगत मुद्दों](https://www.coursera.org/learn/data-science-ethics/home/week) को संबोधित करने के बीच एक अमूर्त अंतर है । / 4) (जैसे ossification, सूचना विषमता, और वितरण संबंधी अनुचितता) जो AI के शस्त्रीकरण को गति दे सकता है ।
-
-बाद वाले को [नैतिक संस्कृतियों को परिभाषित करने के लिए सहयोगात्मक दृष्टिकोण](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-drive-approach-26f451afa29f) की आवश्यकता होती है, जो पूरे संगठनों में भावनात्मक संबंध और सुसंगत साझा मूल्यों का निर्माण करते हैं । यह संगठनों में अधिक [औपचारिक डेटा नैतिकता संस्कृतियों](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) की मांग करता है - _किसी_ को [एंडोन कॉर्ड को खींचने](https://en.wikipedia.org/wiki/Andon_(manufacturing)) की अनुमति देता है (इस प्रक्रिया में नैतिकता संबंधी चिंताओं को जल्दी उठाने के लिए) और एआई परियोजनाओं में _नैतिक मूल्यांकन_ (उदाहरण के लिए, भर्ती में) एक मुख्य मानदंड टीम गठन करना ।
-
----
-## [व्याख्यान के बाद प्रश्नोत्तरी](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/3) 🎯
-## समीक्षा और स्व अध्ययन 
-
-पाठ्यक्रम और पुस्तकें मूल नैतिकता अवधारणाओं और चुनौतियों को समझने में मदद करती हैं, जबकि केस स्टडी और उपकरण वास्तविक दुनिया के संदर्भों में लागू नैतिकता प्रथाओं के साथ मदद करते हैं। शुरू करने के लिए यहां कुछ संसाधन दिए गए हैं।
-
-* [शुरुआती के लिए मशीन लर्निंग](https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md) - Microsoft से निष्पक्षता पर पाठ ।
-* [जिम्मेदार एआई के सिद्धांत](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/) - माइक्रोसॉफ्ट लर्न की ओर से फ्री लर्निंग पाथ ।
-* [नैतिकता और डेटा विज्ञान](https://resources.oreilly.com/examples/0636920203964) - O'Reilly EBook (M. Loukides, H. Mason et. al)
-* [डेटा विज्ञान नैतिकता](https://www.coursera.org/learn/data-science-ethics#syllabus) - मिशिगन विश्वविद्यालय से ऑनलाइन पाठ्यक्रम ।
-* [नैतिकता अलिखित](https://ethicsunwrapped.utexas.edu/case-studies) - टेक्सास विश्वविद्यालय से केस स्टडीज ।
-
-# कार्यभार
-<!-- need to change the link once assignment is translated -->
-[डेटा एथिक्स केस स्टडी लिखें](assignment.hi.md)
diff --git a/1-Introduction/02-ethics/translations/README.ko.md b/1-Introduction/02-ethics/translations/README.ko.md
deleted file mode 100644
index 2711076a..00000000
--- a/1-Introduction/02-ethics/translations/README.ko.md
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-# 데이터 윤리 소개
-
-| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/02-Ethics.png) |
-| :-----------------------------------------------------------------------------------------------: |
-|              데이터 과학 윤리 - _Sketchnote by [@nitya](https://twitter.com/nitya)_               |
-
----
-
-우리는 모두 데이터화된 세계(datafied world)에 살고 있는 데이터 시민(data citizens)입니다.
-
-시장 동향에 따르면 2022년까지 3분의 1 규모의 대규모 조직이 온라인 [마켓플레이스 및 거래소](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/)를 통해 데이터를 사고 팔 것입니다. **앱 개발자**로서 우리는 데이터를 기반으로 한 인사이트(data-driven insight)와 알고리즘 기반 자동화(algorithm-driven automation)를 일상적인 사용자 경험에 통합하는 것이 더 쉽고, 더 저렴하다는 것을 알게 될 것입니다. 그러나 AI가 보편화 됨에 따라, 그러한 알고리즘이 규모적으로 [무기화](https://www.youtube.com/watch?v=TQHs8SA1qpk)로 인한 잠재적 위험을 지니고 있음을 이해해야 합니다.
-
-또한 트렌드에 따르면 우리가 2025년까지 [180 제타 바이트](https://www.statista.com/statistics/871513/worldwide-data-created/) 이상의 데이터를 생성하고 사용할 것을 알려줍니다. **데이터 과학자**로서, 이러한 트렌드는 개인 데이터에 대한 전례 없는 수준의 접근을 제공합니다. 이는 사용자의 행동 프로파일(behavioral profiles)을 구축하고, 우리가 선호하는 결과로 사용자를 유도하는 [자유 선택의 환상](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice)을 만들어내므로 의사결정 과정에 영향을 미칩니다.
-
-데이터 윤리는 이제 데이터 과학 및 데이터 엔지니어링에 _필수적인 가드레일_ 이 되어 데이터 기반 작업으로 인한 잠재적 피해와 의도하지 않은 결과를 최소화하는 데 도움이 됩니다. [가트너(Gartner)의 AI 하이프사이클(Hype Cycle)](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/)은 AI의 _민주화(democratization)_ 와 _산업화(industrialization)_ 에 대한 더 큰 메가트렌드의 핵심 요인으로 디지털 윤리와 관련된 트렌드, 책임감 있는 AI(responsible AI), AI 거버넌스를 가리킵니다.
-
-![가트너(Gartner)의 AI 하이프사이클(Hype Cycle) - 2020](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
-
-이 강의에서는 핵심 개념 및 과제부터 사례 연구 및 거버넌스와 같은 응용 AI 개념에 이르기까지, 데이터와 AI를 사용하여 작업하는 팀과 조직에서 윤리 문화를 확립하는 데 도움이 되는 데이터 윤리의 멋진 영역을 살펴볼 것입니다.
-
-
-
-
-## [강의 전 퀴즈](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/2) 🎯
-
-## 기본 정의
-
-기본 용어를 이해하는 것부터 시작해보겠습니다.
-
-윤리라는 단어는 _성격 또는 본성_ 을 의미하는 (그 어원은 "ethos"인) [그리스어 "ethikos"](https://en.wikipedia.org/wiki/Ethics)에서 유래했습니다.
-
-**윤리**는 사회에서 우리의 행동을 지배하는 공유된 가치와 도덕적 원칙에 관한 것입니다. 윤리는 법에 근거한 것이 아니라
-무엇이 "옳고 그른지"에 대해 널리 받아들여지는 규범에 근거합니다. 그러나 윤리적인 고려 사항은 규정 준수에 대한 더 많은 인센티브를 생성하는 기업 거버넌스 이니셔티브 및 정부 규정에 영향을 미칠 수 있습니다.
-
-**데이터 윤리**는 "_데이터, 알고리즘, 그에 해당하는 실행(practice)_ 과 연관된 도덕적 문제를 측정하고 연구"하는 [윤리의 새로운 분과(branch)](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1)입니다. 여기서 **"데이터"** 는 생성, 기록, 큐레이션, 처리 보급, 공유 및 사용과 관련된 작업에 중점을 두고, **"알고리즘"** 은 AI, 에이전트, 머신러닝 및 로봇에 중점을 둡니다. **"실행(practice)"** 은 책임 있는 혁신, 프로그래밍, 해킹 및 윤리 강령과 같은 주제에 중점을 둡니다.
-
-**응용 윤리**는 [도덕적 고려사항의 실제적인 적용](https://en.wikipedia.org/wiki/Applied_ethics)을 말합니다. 이는 _실제 행동, 제품 및 프로세스_ 의 맥락에서 윤리적 문제를 적극적으로 조사하고 우리가 정의한 윤리적 가치와 일치하도록 수정하는 조치를 취하는 과정입니다.
-
-**윤리 문화**는 우리의 윤리 원칙과 관행이 다음과 같이 채택되도록 [_운영화_ 응용 윤리](https://hbr.org/2019/05/how-to-design-an-ethical-organization)에 관한 것입니다. 조직 전체에 걸쳐 일관되고 확장 가능한 방식. 성공적인 윤리 문화는 조직 전체의 윤리 원칙을 정의하고 준수를 위한 의미 있는 인센티브를 제공하며 조직의 모든 수준에서 바람직한 행동을 장려하고 증폭함으로써 윤리 규범을 강화합니다.
-
-
-## 윤리적 개념
-
-이 섹션에서는 데이터 윤리에 대한 **공유 가치**(원칙) 및 **윤리적 과제**(문제)와 같은 개념을 논의하고 이러한 개념을 이해하는 데 도움이 되는 **케이스 스터디**를 살펴볼 것입니다.
-
-### 1. 윤리 원칙
-
-모든 데이터 윤리에 대한 전략은 _윤리 원칙_-데이터 및 AI 프로젝트에서, 허용되는 행동을 설명하고 규정 준수 조치에 대해 설명하는 "공유된 가치"-이 무엇인지 정의하는 것으로부터 시작됩니다. 개인 또는 팀 단위로 정의할 수 있습니다. 그러나 대부분의 대규모 조직은 이런 _윤리적인 AI_ 의 Mission 선언문이나 프레임워크를 회사 차원에서 정의하고, 모든 팀에 일관되게 시행하므로 간략하게 정의합니다.
-
-**예시:** 마이크로소프트의 [책임있는 AI](https://www.microsoft.com/en-us/ai/responsible-ai) Mission 선언문은 다음과 같습니다: _"우리는 사람을 최우선으로 하는 융리 원칙에 따라 AI 기반의 발전에 전념합니다."_ - 아래 프레임워크에서 6가지 윤리 원칙을 식별합니다.
-
-![Microsoft의 책임있는 AI](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png)
-
-이러한 원칙을 간략하게 살펴보겠습니다. _투명성_ 과 _책임성_ 은 다른 원칙들의 기반이 되는 기본적인 가치입니다. 여기에서부터 시작하겠습니다.
-
-* [**책임**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)은 실무자가 데이터 및 AI 운영과 이러한 윤리적 원칙 준수에 대해 _책임_ 을 지도록 합니다.
-* [**투명성**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)은 데이터 및 AI 작업이 사용자에게 _이해 가능_(해석 가능)하도록 보장하여 결정의 배경과 이유를 설명합니다.
-* [**공평성**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) - AI가 _모든 사람_ 을 공정하게 대하도록 하는 데 중점을 두고, 데이터 및 시스템의 모든 시스템적 또는 암묵적 사회∙기술적 편견을 해결합니다.
-* [**신뢰성 & 안전**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)은 AI가 정의된 값으로 _일관되게_ 동작하도록 하여 잠재적인 피해나 의도하지 않은 결과를 최소화합니다.
-* [**프라이버시 & 보안**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)는 데이터 계보(Data Lineage)를 이해하고, 사용자에게 _데이터 개인 정보 보호 및 관련 보호 기능_ 을 제공하는 것입니다.
-* [**포용**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)은 AI 솔루션을 의도적으로 설계하고 _광범위한 인간의 요구_ 와 기능을 충족하도록 조정하는 것 입니다.
-
-> 🚨 데이터 윤리 Mission 선언문이 무엇인지 생각해보십시오. 다른 조직의 윤리적 AI 프레임워크를 탐색해보세요. - 다음과 같은 예시가 있습니다. [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles) ,and [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/). 이들의 공통점은 무엇입니까? 이러한 원칙은 그들이 운영하는 AI 제품 또는 산업과 어떤 관련이 있습니까?
-
-### 2. 윤리적 과제
-
-윤리적 원칙이 정의되면 다음 단계는 데이터와 AI 작업을 평가하여 이러한 공유 가치와 일치하는지 확인하는 것입니다. _데이터 수집_ 과 _알고리즘 디자인_, 이 두 가지 범주에서 당신의 행동(Action)을 생각해 보십시오.
-
-데이터 수집을 통해, 그 행동에는 식별 가능한(idenitifiable) 살아있는 개인에 대한 **개인 데이터** 또는 개인 식별 정보(PII, Personally Identifiable Information)이 포함될 수 있습니다. 여기에는 종합적으로 개인을 식별할 수 있는 [비개인 데이터의 다양한 항목](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en)도 포함됩니다. 윤리적인 문제는 _데이터 프라이버시(개인 정보 보호)_, _데이터 소유권(ownership)_, 그리고 사용자의 _정보 제공 동의_ 와 _지적 재산권_ 과 같은 관련된 주제와 연관될 수 있습니다.
-
-알고리즘 설계(design)을 사용하면, **데이터 셋**을 수집 및 선별란 다음 이를 사용하여 결과를 예측하거나 실제 상황에서 의사결정을 자동화하는 **데이터 모델**을 교육 및 배포하는 작업이 포함됩니다. 윤리적인 문제는 본질적으로 시스템적인 일부 문제를 포함하여 알고리즘의 _데이터 셋 편향_, _데이터 품질_ 문제, _불공정_ 및 _잘못된 표현_ 으로 인해 발생할 수 있습니다.
-
-두 경우 모두 윤리 문제는 우리의 행동이 공유 가치와 충돌할 수 있는 영역을 강조합니다. 이러한 우려를 감지, 완화, 최소화 또는 제거하려면 우리의 행동과 관련된 도덕적 "예/아니오" 질문을 하고 필요에 따라 수정 조치를 취하십시오. 몇 가지 윤리적 챌린지와 그것이 제기하는 도덕적 질문을 살펴보겠습니다.
-
-
-#### 2.1 데이터 소유권
-
-데이터 수집에는 종종 데이터 주체를 식별할 수 있는 개인 데이터가 포함됩니다. [데이터 소유권](https://permission.io/blog/data-ownership)은 데이터의 생성, 처리 및 보급과 관련된 _제어(control)_ 와 [_사용자 권한_](https://permission.io/blog/data-ownership)에 관한 것입니다.
-
-우리가 물어야 할 도덕적 질문은 다음과 같습니다.: 
- * 누가 데이터를 소유합니까? (사용자 또는 조직)
- * 데이터 주체(data subjects)는 어떤 권리를 가지고 있나요? (예: 접근, 삭제, 이동성)
- * 조직은 어떤 권리를 가지고 있습니까? (예: 악의적인 사용자 리뷰 수정)
-
-#### 2.2 정보 제공 동의
-
-[정보 제공 동의](https://legaldictionary.net/informed-consent/)는 목적, 잠재적 위험 및 대안을 포함한 관련 사실을 _완전히 이해_ 한 사용자가 데이터 수집과 같은 조치에 동의하는 행위를 말합니다.
-
-여기에서 탐색할 질문은 다음과 같습니다.:
- * 사용자(데이터 주체)가 데이터 캡처 및 사용에 대한 권한을 부여했습니까?
- * 사용자가 해당 데이터가 수집된 목적을 이해했습니까?
- * 사용자가 참여로 인한 잠재적 위험을 이해했습니까?
-
-#### 2.3 지적 재산권
-
-[지적 재산권](https://en.wikipedia.org/wiki/Intellectual_property)은 인간의 주도(human initiative)로 인해 생긴 개인이나 기업에 _경제적 가치가 있을 수 있는_ 무형의 창조물을 말합니다. 
-
-여기에서 탐색할 질문은 다음과 같습니다:
- * 수집된 데이터가 사용자나 비즈니스에 경제적 가치가 있었습니까?
- * **사용자**가 여기에 지적 재산권을 가지고 있습니까?
- * **조직**에 지적 재산권이 있습니까?
- * 이러한 권리가 존재한다면, 어떻게 보호가 됩니까?
-
-#### 2.4 데이터 프라이버시
-
-[데이터 프라이버시](https://www.northeastern.edu/graduate/blog/what-is-data-privacy/) 또는 정보 프라이버시는 개인 식별 정보에 대한 사용자 개인 정보 보호 및 사용자 신원 보호를 의미합니다.
-
-여기서 살펴볼 질문은 다음과 같습니다:
- * 사용자(개인) 데이터는 해킹 및 유출로부터 안전하게 보호되고 있습니까?
- * 승인된 사용자 및 컨텍스트만 사용자 데이터에 액세스할 수 있습니까?
- * 데이터를 공유하거나 유포할 때 사용자의 익명성이 유지됩니까?
- * 익명화된 데이터 세트에서 사용자를 익명화할 수 있습니까?
-
-
-#### 2.5 잊혀질 권리
-
-[잊혀질 권리](https://en.wikipedia.org/wiki/Right_to_be_forgotten) 또는 [삭제할 권리](https://www.gdpreu.org/right-to-be-forgotten/)는 사용자에 대한 추가적인 개인 데이터 보호를 제공합니다. 특히, 사용자에게 _특정 상황에서_ 인터넷 검색 및 기타 위치에서 개인 데이터 삭제 또는 제거를 요청할 수 있는 권리를 부여하여, 사용자가 과거 조치(action)를 취하지 않고 온라인에서 새로운 출발을 할 수 있게 합니다.
-
-여기서는 다음 질문들을 살펴볼 것입니다:
- * 시스템에서 데이터 주체(Data Subject)가 삭제를 요청할 수 있습니까?
- * 사용자 동의 철회 시 자동으로 데이터를 삭제해야 하나요?
- * 데이터가 동의 없이 또는 불법적인 방법으로 수집되었나요?
- * 우리는 데이터 개인 정보 보호에 대한 정부 규정을 준수합니까?
-
-
-#### 2.6 데이터셋 편향(Bias)
-
-데이터셋 또는 [데이터 콜렉션 편향](http://researcharticles.com/index.php/bias-in-data-collection-in-research/)은 알고리즘 개발을 위해 _대표적이지 않은(non-representative)_ 데이터 하위 집합을 선택하여, 다양한 그룹의 결과에서 잠재적인 불공정이 발생하는 것에 관한 것입니다. 편향의 유형에는 선택 또는 샘플링 편향, 자원자 편향, 도구 편향이 있습니다.
-
-여기서는 다음 질문들을 살펴볼 것입니다:
- * 데이터 주체의 대표적인 데이터들을 모집했는가?
- * 다양한 편향에 대해 수집되거나 선별된 데이터 셋을 테스트 했습니까?
- * 발견된 편향을 완화하거나 제거할 수 있습니까?
-
-#### 2.7 데이터 품질
-
-[데이터 품질](https://lakefs.io/data-quality-testing/)은 알고리즘을 개발하는 데 사용된 선별된 데이터 셋의 유효성을 살펴보고, 기능과 레코드가 우리의 AI 목적에 필요한 정확성 및 일관성 수준에 대한 요구사항을 충족하는 지 확인합니다.
-
-여기서는 다음 질문들을 살펴볼 것입니다:
- * 유스케이스(use case)에 대한 유효한 _기능_ 을 캡처했습니까?
- * 다양한 데이터 소스에서 데이터가 _일관되게_ 캡처되었습니까?
- * 데이터셋은 다양한 조건 또는 시나리오에 대해 _완전_ 합니까?
- * 포착된 정보가 현실을 _정확하게_ 반영합니까?
-
-#### 2.8 알고리즘 공정성
-
-[알고리즘 공정성](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f)은, _할당(해당 그룹에서 리소스가 거부되거나 보류되는 경우)_ 및 _서비스 품질(일부 하위 그룹의 경우 AI가 다른 그룹의 경우만큼 정확하지 않음)_ 에서, 알고리즘 설계가 [잠재적인 피해](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml)로 이어지는 데이터 주체의 특정 하위 그룹을 체계적으로 구별하는지 확인합니다.
-
-여기서는 다음 질문들을 살펴볼 것입니다:
- * 다양한 하위 그룹 및 조건에 대해 모델 정확도를 평가했습니까?
- * 잠재적인 피해(예: 고정 관념)에 대해 시스템을 면밀히 조사했습니까?
- * 식별된 피해를 완화하기 위해 데이터를 수정하거나 모델을 다시 학습시킬 수 있습니까?
-
-더 알아보고 싶다면, 다음 자료를 살펴보세요: [AI 공정성 체크리스트](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4t6dA)
-
-#### 2.9 와전(Misrepresentation)
-
-[데이터 와전(Misrepresentation)](https://www.sciencedirect.com/topics/computer-science/misrepresentation)은 정직하게 보고된 데이터의 통찰력을, 원하는 내러티브(Narrative)에 맞춰 기만적인 방식으로 전달하고 있는지 묻는 것입니다.
-
-여기서는 다음 질문들을 살펴볼 것입니다:
- * 불완전하거나 부정확한 데이터를 보고하고 있습니까?
- * 오해의 소지가 있는 결론을 도출하는 방식으로 데이터를 시각화하고 있습니까?
- * 결과를 조작하기 위해 선택적 통계 기법을 사용하고 있습니까?
- * 다른 결론을 제시할 수 있는 대안적인 설명이 있습니까?
-
-#### 2.10 자유로운 선택
-[자유롭게 선택하고 있다는 환상](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice)은 시스템 '선택 아키텍처'가 의사결정 알고리즘을 사용하여 사람들에게 선택권과 통제권을 주는 것처럼 하면서 시스템이 선호하는 결과를 선택하도록 유도할 때 발생합니다. 이런 [다크 패턴(dark pattern)](https://www.darkpatterns.org/)은 사용자에게 사회적, 경제적 피해를 줄 수 있습니다. 사용자 결정은 행동 프로파일에 영향을 미치기 때문에, 이러한 행동은 잠재적으로 이러한 피해의 영향을 증폭하거나 확장할 수 있는 향후의 선택을 유도합니다.
-
-여기서는 다음 질문들을 살펴볼 것입니다:
- * 사용자는 그 선택의 의미를 이해했습니까?
- * 사용자는 (대안이 되는) 선택과 각각의 장단점을 알고 있습니까?
- * 사용자가 나중에 자동화되거나 영향을 받은 선택을 되돌릴 수 있습니까?
-
-### 3. 케이스 스터디
-
-이러한 윤리적 문제를 실제 상황에 적용하려면, 그러한 윤리 위반이 간과 되었을 때 개인과 사회에 미칠 잠재적인 피해와 결과를 강조하는 케이스 스터디를 살펴보는 것이 도움이 됩니다. 
-
-다음은 몇 가지 예입니다.
-
-| 윤리적 과제                    | Case Study                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
-| ------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **통보 동의** | 1972 - [Tuskegee 매독 연구](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - 피험자로 연구에 참여한 아프리카계 미국인 남성은 피험자에게 진단이나 정보를 알려주지 않은 연구원들에게 무료 의료 서비스를 약속받았지만, 약속은 지켜지지 않았다. 많은 피험자가 사망하고 배우자와 자녀들이 영향을 받았습니다. 연구는 40년 동안 지속되었습니다.                                                                                                                                                   |
-| **데이터 프라이버시(Privacy)**               | 2007 - [넷플릭스 Data Prize](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) 는 추천 알고리즘을 개선하기 위해 연구원들에게 _5만명 고객으로부터 수집한 1천만개의 비식별화된(anonymized) 영화 순위_를 제공했습니다. 그러나 연구원들은 비식별화된(anonymized) 데이터를 _외부 데이터셋_ (예를 들어, IMDb 댓글)에 있는 개인식별 데이터(personally-identifiable data)와 연관시킴으로, 효과적으로 일부 Netflix 가입자를 '비익명화(de-anonymizing)' 할 수 있었습니다. |
-| **편향 수집**            | 2013 - 보스턴 시는 시민들이 움푹 들어간 곳을 보고할 수 있는 앱인 [Street Bump](https://www.boston.gov/transportation/street-bump)를 개발하여 시에서 문제를 찾고 수정할 수 있는 더 나은 도로 데이터를 제공합니다. 그러나 [저소득층의 사람들은 자동차와 전화에 대한 접근성이 낮기 때문에](https://hbr.org/2013/04/the-hidden-biases-in-big-data) 이 앱에서 도로 문제를 볼 수 없었습니다. 개발자들은 학계와 협력하여 공정성을 위한 _공평한 접근 및 디지털 격차_ 문제를 해결했습니다. |
-| **알고리즘 공정성** | 2018 - MIT [성별 유색인종 연구](http://gendershades.org/overview.html)에서 성별 분류 AI 제품의 정확도를 평가하여 여성과 유색인의 정확도 격차를 드러냈습니다. [2019년도 Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/)는 남성보다 여성에게 신용을 덜 제공하는 것으로 보입니다. 둘 다 사회 경제적 피해로 이어지는 알고리즘 편향의 문제를 나타냅니다.  |
-| **데이터 허위 진술** | 2020년 - [조지아 보건부 코로나19 차트 발표](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid- 19건-거절-재개)의 x축이 시간순이 아닌 순서로 표시된 확인된 사례의 추세에 대해 시민들을 잘못된 방향으로 이끄는 것으로 나타났습니다. 이 발표 시각화 트릭을 통해 잘못된 표현을 나타냈습니다. |
-| **자유 선택의 환상** | 2020 - 학습 앱인 [ABCmouse는 부모들이 취소할 수 없는 구독료에 빠지게 되는 FTC 불만 해결을 위해 1천만 달러 지불](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) 했습니다. 이는 사용자가 잠재적으로 해로운 선택을 하도록 유도하는 선택 아키텍처의 어두운 패턴을 보여줍니다. |
-| **데이터 개인정보 보호 및 사용자 권한** | 2021 - Facebook 의 [데이터 침해](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify- 사용자) 는 5억 3천만 명의 사용자의 데이터를 노출하여 FTC에 50억 달러의 합의금을 냈습니다. 그러나 데이터 투명성 및 액세스에 대한 사용자 권한을 위반하는 위반 사항을 사용자에게 알리는 것을 거부했습니다. |
-
-더 많은 사례 연구를 살펴보고 싶으십니까? 다음 리소스를 확인하세요.:
-* [윤리를 풀다(ethic unwrapped)](https://ethicsunwrapped.utexas.edu/case-studies) - 다양한 산업 분야의 윤리 딜레마
-* [데이터 과학 윤리 과정](https://www.coursera.org/learn/data-science-ethics#syllabus) - 획기적인 사례 연구 탐구
-* [문제가 발생한 곳](https://deon.drivendata.org/examples/) - 사례와 함께 살펴보는 데온(deon)의 체크리스트
-
-> 🚨 당신이 본 사례 연구에 대해 생각해보십시오. 당신은 당신의 삶에서 유사한 윤리적 도전을 경험했거나 영향을 받은 적이 있습니까? 이 섹션에서 논의한 윤리적 문제 중 하나에 대한 다른 사례 연구를 하나 이상 생각할 수 있습니까?
-
-## 응용 윤리(Applied Ethics)
-
-우리는 실제 상황에서 윤리 개념, 도전 과제 및 사례 연구에 대해 이야기했습니다. 그러나 프로젝트에서 윤리적 원칙과 관행을 _적용_ 하기 시작하려면 어떻게 해야 합니까? 그리고 더 나은 거버넌스를 위해 이러한 관행을 어떻게 _운영_ 할 수 있습니까? 몇 가지 실제 솔루션을 살펴보겠습니다: 
-
-### 1. 전문 코드(Professional Codes)
-
-전문 강령(Professional Codes)은 조직이 구성원의 윤리 원칙과 사명 선언문을 지지하도록 "인센티브"를 제공하는 하나의 옵션을 제공합니다. 강령은 직원이나 구성원이 조직의 원칙에 부합하는 결정을 내리는 데 도움이 되는 직업적 행동에 대한 _도덕적 지침_ 입니다. 이는 회원들의 자발적인 준수에 달려 있습니다. 그러나 많은 조직에서 구성원의 규정 준수를 유도하기 위해 추가 보상과 처벌을 제공합니다.
-
-다음과 같은 사례가 있습니다:
-
- * [Oxford Munich](http://www.code-of-ethics.org/code-of-conduct/) 윤리강령
- * [데이터 과학 협회](http://datascienceassn.org/code-of-conduct.html) 행동강령 (2013년 제정)
- * [ACM 윤리 및 직업 행동 강령](https://www.acm.org/code-of-ethics) (1993년 이후)
-
-> 🚨 전문 엔지니어링 또는 데이터 과학 조직에 속해 있습니까? 그들의 사이트를 탐색하여 그들이 직업적 윤리 강령을 정의하는지 확인하십시오. 이것은 그들의 윤리적 원칙에 대해 무엇을 말합니까? 구성원들이 코드를 따르도록 "인센티브"를 제공하는 방법은 무엇입니까?
-
-### 2. 윤리 체크리스트
-
-전문 강령은 실무자에게 필요한 _윤리적 행동_ 을 정의하지만 특히 대규모 프로젝트 시행에서 [자주 사용되는 제한 사항이 있습니다](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md). 이로 인해 많은 데이터 과학 전문가들이 [체크리스트를 따름으로](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) 보다 결정적이고 실행 가능한 방식으로 **원칙과 사례를 연결** 할 수 있습니다.
-
-체크리스트는 질문을 운영 가능한 "예/아니오" 작업으로 변환하여 표준 제품 릴리스 워크플로의 일부로 추적할 수 있도록 합니다.
-
-다음과 같은 사례가 있습니다:
- * [Deon](https://deon.drivendata.org/) - 쉬운 통합을 위한 Command Line Tool 형태의 범용적인 윤리 체크리스트 ([업계 권고사항](https://deon.drivedata.org/#checklist-citations)에서 만들어짐)
- * [개인정보 감사 체크리스트](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - 법적 및 사회적 노출 관점에서 정보 처리 관행에 대한 일반적인 지침을 제공합니다.
- * [AI 공정성 체크리스트](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) - 공정성 검사의 채택 및 AI 개발 주기 통합을 지원하기 위해 AI 실무자가 작성.
- * [데이터 및 AI의 윤리에 대한 22가지 질문](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - 디자인, 구현 및 조직적 맥락에서 윤리적 문제의 초기 탐색을 위한, 보다 개방적인 프레임워크, 구조화.
-
-### 3. 윤리 규정
-
-윤리는 공유 가치를 정의하고 옳은 일을 _자발적으로_ 하는 것입니다. **규정 준수**는 정의된 경우 _법률 준수_ 에 관한 것입니다. **거버넌스**는 조직이 윤리 원칙을 시행하고 확립된 법률을 준수하기 위해 운영하는 모든 방식을 광범위하게 포함합니다.
-
-오늘날 거버넌스는 조직 내에서 두 가지 형태를 취합니다. 첫째, **윤리적 AI** 원칙을 정의하고 조직의 모든 AI 관련 프로젝트에서 채택을 운영하기 위한 관행을 수립하는 것입니다. 둘째, 사업을 영위하는 지역에 대해 정부에서 의무화한 모든 **데이터 보호 규정**을 준수하는 것입니다.
-
-데이터 보호 및 개인 정보 보호 규정 사례:
-
- * `1974`, [미국 개인 정보 보호법](https://www.justice.gov/opcl/privacy-act-1974) - _연방 정부_ 의 개인 정보 수집, 사용 및 공개를 규제합니다.
- * `1996`, [미국 HIPAA(Health Insurance Portability & Accountability Act)](https://www.cdc.gov/phlp/publications/topic/hipaa.html) - 개인 건강 데이터를 보호합니다.
- * `1998`, [미국 아동 온라인 개인정보 보호법(COPPA)](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule) - 13세 미만 어린이의 데이터 프라이버시를 보호합니다.
- * `2018`, [GDPR(일반 데이터 보호 규정)](https://gdpr-info.eu/) - 사용자 권한, 데이터 보호 및 개인 정보 보호를 제공합니다.
- * `2018`, [캘리포니아 소비자 개인정보 보호법(CCPA)](https://www.oag.ca.gov/privacy/ccpa) 소비자에게 자신의 (개인) 데이터에 대해 더 많은 _권리_ 를 부여합니다.
- * `2021`, 중국의 [개인정보보호법](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/) 막 통과되어 전 세계적으로 가장 강력한 온라인 데이터 개인 정보 보호 규정 중 하나를 만들었습니다.
-
-> 🚨 유럽 연합에서 정의한 GDPR(일반 데이터 보호 규정)은 오늘날 가장 영향력 있는 데이터 개인 정보 보호 규정 중 하나입니다. 시민의 디지털 프라이버시와 개인 데이터를 보호하기 위헌 [8가지 사용자 권한](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr)도 정의하고 있다는 사실을 알고 계셨습니까? 이것이 무엇이며 왜 중요한지 알아보십시오.
-
-
-### 4. 윤리 문화
-
-_준수_ ("법규"를 충족하기 위해 충분히 노력함)와 (골화, 정보 비대칭 및 분배 불공정과 같은) AI의 무기화를 가속화할 수 있는 [시스템 문제](https://www.coursera.org/learn/data-science-ethics/home/week) 해결 사이에는 무형의 격차가 있습니다.
-
-후자는 산업에서 _조직 전체적으로_ 정서적 연결과 일관된 공유 가치를 구축하는 [윤리 문화를 정의하기 위한 협력적 접근 방식](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f)이 필요합니다. 이것은 조직에서 더 많은 [공식화된 데이터 윤리 문화](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/)를 요구합니다. 이런 문화는 _누구나_ (프로세스 초기에 윤리 문제 제기를 위해) [Andon 강령을 사용하고](https://en.wikipedia.org/wiki/Andon_(manufacturing)) _윤리적 평가_ (예: 고용 시)를 AI 프로젝트의 핵심 기준 팀 구성으로 만듭니다.
-
----
-## [강의 후 퀴즈](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/3) 🎯
-## 복습 & 독학
-
-과정과 책은 핵심 윤리 개념과 과제를 이해하는 데 도움이 되며, Case Study와 도구는 실제 상황에서 윤리 사항들을 적용하는 데 도움이 됩니다. 다음은 시작을 할 때 도움이 되는 몇가지 자료들입니다.
-
-* [초보자를 위한 기계 학습](https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md) - 공정성(fairness)에 대한 강의, from Microsoft.
-* [책임있는 AI 원칙](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/) - 무료 학습 경로, from Microsoft Learn.
-* [윤리와 데이터 과학](https://resources.oreilly.com/examples/0636920203964) - O'Reilly EBook (M. Loukides, H. Mason et. al)
-* [데이터 과학 윤리](https://www.coursera.org/learn/data-science-ethics#syllabus) - 미시간 대학의 온라인 학습 과정.
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - 텍사스 대의 Case Study.
-
-# 과제
-
-[데이터 윤리 Case Study 작성](./assignment.ko.md)
\ No newline at end of file
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-# Inleiding tot gegevensethiek
-
-|![ Sketchnote door [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/02-Ethics.png)|
-|:---:|
-| Ethiek van gegevenswetenschap - _Sketchnote door [@nitya](https://twitter.com/nitya)_ |
-
----
-
-We zijn allemaal databurgers die in een datafied wereld leven.
-
-Markttrends vertellen ons dat tegen 2022 1-op-3 grote organisaties hun data zullen kopen en verkopen via online [Marketplaces and Exchanges](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020). Als **App-ontwikkelaars** zullen we het gemakkelijker en goedkoper vinden om gegevensgestuurde inzichten en algoritmegestuurde automatisering te integreren in dagelijkse gebruikerservaringen. Maar naarmate AI steeds meer voorkomt, moeten we ook de mogelijke schade begrijpen die wordt veroorzaakt door de [bewapening](https://www.youtube.com/watch?v=TQHs8SA1qpk) van dergelijke algoritmen op grote schaal.
-
-Trends geven ook aan dat we tegen 2025 meer dan [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) aan gegevens zullen creëren en consumeren. Als **Data Scientists** geeft dit ons ongekende niveaus van toegang tot persoonlijke gegevens. Dit betekent dat we gedragsprofielen van gebruikers kunnen maken en de besluitvorming kunnen beïnvloeden op een manier die een [illusie van vrije keuze](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) creëert, terwijl gebruikers aangespoord worden naar resultaten die onze voorkeur hebben. Het roept ook bredere vragen op over gegevensprivacy en gebruikersbescherming.
-
-Gegevensethiek is nu een _noodzakelijke vangrails_ voor datawetenschap en -engineering, waardoor we mogelijke schade en onbedoelde gevolgen van onze gegevensgestuurde acties kunnen minimaliseren. De [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identificeert relevante trends in digitale ethiek, verantwoorde AI en AI-governance als belangrijke drijfveren voor grotere megatrends rond _democratisering_ en _industrialisering_ van AI.
-
-![Gartner's hypecyclus voor AI - 2020](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
-
-In deze les verkennen we het fascinerende gebied van data-ethiek - van kernconcepten en uitdagingen tot casestudy's en toegepaste AI-concepten zoals governance - die helpen een ethische cultuur tot stand te brengen in teams en organisaties die met data en AI werken.
-
-
-
-
-## [Pre-college quiz](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/2) 🎯
-
-## Basisdefinities
-
-Laten we beginnen met het begrijpen van de basisterminologie.
-
-Het woord "ethiek" komt van het [Griekse woord "ethikos"](https://en.wikipedia.org/wiki/Ethics) (en de wortel "ethos") wat _karakter of morele aard_ betekent.
-
-**Ethiek** gaat over de gedeelde waarden en morele principes die ons gedrag in de samenleving bepalen. Ethiek is niet gebaseerd op wetten maar op
-algemeen aanvaarde normen van wat "goed versus fout" is. Ethische overwegingen kunnen echter van invloed zijn op initiatieven op het gebied van corporate governance en overheidsregelgeving die meer prikkels voor compliance creëren.
-
-**Data-ethiek** is een [nieuwe tak van ethiek](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) die "morele problemen met betrekking tot _data, algoritmen en overeenkomstige praktijken_" bestudeerd. Hier richt **"data"** zich op acties met betrekking tot het genereren, opnemen, beheren, verwerken, verspreiden, delen en gebruiken. **"Algoritmen"** richt zich op AI, agents, machine learning en robots, en **"practices"** richt zich op onderwerpen als verantwoord innoveren, programmeren, hacken en ethische codes.
-
-**Toegepaste ethiek** is de [praktische toepassing van morele overwegingen](https://en.wikipedia.org/wiki/Applied_ethics). Het is het proces van het actief onderzoeken van ethische kwesties in de context van _real-world acties, producten en processen_, en het nemen van corrigerende maatregelen om ervoor te zorgen dat deze in overeenstemming blijven met onze gedefinieerde ethische waarden.
-
-**Ethische cultuur** gaat over [toegepaste ethiek _activeren_](https://hbr.org/2019/05/how-to-design-an-ethical-organization) om ervoor te zorgen dat onze ethische principes en praktijken worden toegepast in een consistente en schaalbare manier door de hele organisatie heen. Succesvolle ethische culturen definiëren organisatiebrede ethische principes, bieden zinvolle prikkels voor naleving en versterken ethische normen door gewenst gedrag op elk niveau van de organisatie aan te moedigen en te versterken.
-
-
-## Ethische concepten
-
-In dit gedeelte bespreken we concepten als **gedeelde waarden** (principes) en **ethische uitdagingen** (problemen) voor data-ethiek - en onderzoeken we **casestudy's** die je helpen deze concepten in context van de echte wereld te begrijpen.
-
-### 1. Ethische principes
-
-Elke data-ethiekstrategie begint met het definiëren van _ethische principes_ - de 'gedeelde waarden' die acceptabel gedrag beschrijven en richting geven aan daaraan conformerende acties in onze data- en AI-projecten. Je kunt deze op individueel of teamniveau definiëren. De meeste grote organisaties schetsen deze echter in een _ethische AI_-missieverklaring of -kader dat op bedrijfsniveau is gedefinieerd en consistent wordt gehandhaafd in alle teams.
-
-**Voorbeeld:** Microsoft's [Responsible AI](https://www.microsoft.com/en-us/ai/responsible-ai) mission statement luidt: _"We zetten ons in voor de vooruitgang van AI-driven door ethische principes die de mens centraal stellen"_ - door middel van 6 ethische principes in het onderstaande kader:
-
-![Verantwoordelijke AI bij Microsoft](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png)
-
-Laten we deze principes kort onderzoeken. _Transparantie_ en _accountability_ zijn fundamentele waarden waarop andere principes voortbouwen - dus laten we daar beginnen:
-
-* [**Verantwoording**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) maakt beoefenaars _verantwoordelijk_ voor hun gegevens- en AI-operaties, en naleving van deze ethische principes.
-* [**Transparantie**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) zorgt ervoor dat gegevens en AI-acties _begrijpelijk_ (interpreteerbaar) zijn voor gebruikers, zoals het wat en waarom achter beslissingen.
-* [**Eerlijkheid**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) - richt zich op het waarborgen dat AI _alle mensen_ eerlijk behandelt, waarbij alle systemische of impliciete socio-technische vooroordelen in data en systemen worden geaddresseerd.
-* [**Betrouwbaarheid en veiligheid**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - zorgt ervoor dat AI zich _consistent_ gedraagt ​​met gedefinieerde waarden, waardoor potentiële schade of onbedoelde gevolgen worden geminimaliseerd.
-* [**Privacy en beveiliging**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - gaat over het begrijpen van gegevensafstamming en het bieden van _gegevensprivacy en gerelateerde beschermingen_ voor gebruikers.
-* [**Inclusiviteit**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - gaat over het ontwerpen van AI-oplossingen met intentie, ze aanpassen om te voldoen aan een _brede scala aan menselijke behoeften_ en mogelijkheden.
-
-> 🚨 Denk na over wat jouw missie voor gegevensethiek zou kunnen zijn. Verken ethische AI-kaders van andere organisaties - hier zijn voorbeelden van [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles), en [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/). Welke gedeelde waarden hebben ze gemeen? Hoe verhouden deze principes zich tot het AI-product of de industrie waarin ze actief zijn?
-
-### 2. Ethische uitdagingen
-
-Zodra we ethische principes hebben gedefinieerd, is de volgende stap het evalueren van onze gegevens en AI-acties om te zien of ze in overeenstemming zijn met die gedeelde waarden. Denk na over je acties in twee categorieën: _gegevensverzameling_ en _algoritmeontwerp_.
-
-Bij het verzamelen van gegevens zullen acties waarschijnlijk betrekking hebben op **persoonlijke gegevens** of persoonlijk identificeerbare informatie (PII) voor identificeerbare levende personen. Dit omvat [diverse items van niet-persoonlijke gegevens](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en) die _collectief_ een persoon identificeren. Ethische uitdagingen kunnen betrekking hebben op _gegevensprivacy_, _eigendom van gegevens_ en gerelateerde onderwerpen zoals _geïnformeerde toestemming_ en _intellectuele eigendomsrechten_ voor gebruikers.
-
-Bij het ontwerpen van algoritmen zullen acties bestaan ​​uit het verzamelen en beheren van **datasets**, en deze vervolgens gebruiken om **datamodellen** te trainen en in te zetten die resultaten voorspellen of beslissingen automatiseren in reële contexten. Ethische uitdagingen kunnen ontstaan ​​door _dataset bias_, _data quality_ issues, _unfairness_ en _misrepresentation_ (onvoldoende vertegenwoordiging) in algoritmen - inclusief enkele problemen die systemisch van aard zijn.
-
-In beide gevallen wijzen ethische uitdagingen op gebieden waar onze acties in conflict kunnen komen met onze gedeelde waarden. Om deze zorgen op te sporen, te verminderen, te minimaliseren of weg te nemen, moeten we morele "ja/nee"-vragen stellen met betrekking tot onze acties en vervolgens corrigerende maatregelen nemen als dat nodig is. Laten we eens kijken naar enkele ethische uitdagingen en de morele vragen die ze oproepen:
-
-#### 2.1 Gegevenseigendom
-
-Bij het verzamelen van gegevens gaat het vaak om persoonsgegevens die de betrokkenen kunnen identificeren. [Data-eigendom](https://permission.io/blog/data-ownership) gaat over _controle_ en [_gebruikers rechten_](https://permission.io/blog/data-ownership) met betrekking tot het aanmaken, verwerken en verspreiding van gegevens.
-
-De morele vragen die we moeten stellen zijn:
- * Wie is eigenaar van de gegevens? (gebruiker of organisatie)
- * Welke rechten hebben betrokkenen? (bijvoorbeeld: toegang, wissen, overdraagbaarheid)
- * Welke rechten hebben organisaties? (bijvoorbeeld: kwaadwillende gebruikersrecensies corrigeren)
-
-#### 2.2 Geïnformeerde toestemming
-
-[Informed consent](https://legaldictionary.net/informed-consent/) definieert de handeling van gebruikers die instemmen met een actie (zoals gegevensverzameling) met een _volledig begrip_ van relevante feiten, waaronder het doel, potentiële risico's en alternatieven.
-
-Vragen om hier te onderzoeken zijn:
- * Heeft de gebruiker (betrokkene) toestemming gegeven voor het vastleggen en gebruiken van gegevens?
- * Begreep de gebruiker het doel waarvoor die gegevens werden vastgelegd?
- * Begreep de gebruiker de mogelijke risico's van zijn deelname?
-
-#### 2.3 Intellectuele eigendom
-
-[Intellectuele eigendom](https://en.wikipedia.org/wiki/Intellectual_property) verwijst naar immateriële creaties die het resultaat zijn van menselijk initiatief en die _economische waarde_ kunnen hebben voor individuen of bedrijven.
-
-Vragen om hier te onderzoeken zijn:
- * Hadden de verzamelde gegevens economische waarde voor een gebruiker of bedrijf?
- * Heeft de **gebruiker** hier intellectueel eigendom?
- * Heeft de **organisatie** hier intellectueel eigendom?
- * Als deze rechten bestaan, hoe beschermen we ze dan?
-
-#### 2.4 Gegevensprivacy
-
-[Data privacy](https://www.northeastern.edu/graduate/blog/what-is-data-privacy/) of informatieprivacy verwijst naar het behoud van de gebruikersprivacy en bescherming van de gebruikersidentiteit met betrekking tot persoonlijk identificeerbare informatie.
-
-Vragen om hier te onderzoeken zijn:
- * Zijn (persoons)gegevens van gebruikers beveiligd tegen hacks en lekken?
- * Zijn gebruikersgegevens alleen toegankelijk voor geautoriseerde gebruikers en contexten?
- * Blijft de anonimiteit van gebruikers behouden wanneer gegevens worden gedeeld of verspreid?
- * Kan een gebruiker worden geanonimiseerd voor een geanonimiseerde datasets?
-
-#### 2.5 Recht om vergeten te worden
-
-Het [Recht om te worden vergeten](https://en.wikipedia.org/wiki/Right_to_be_forgotten) of [Recht tot verwijdering](https://www.gdpreu.org/right-to-be-forgotten/) biedt aanvullende bescherming van persoonsgegevens voor gebruikers. Het geeft gebruikers met name het recht om _onder specifieke omstandigheden_ persoonlijke gegevens van zoekopdrachten op internet en andere locaties te verzoeken tot wissen of verwijdering van persoonlijke gegevens, waardoor ze een nieuwe start online kunnen maken zonder dat er eerdere acties tegen hen worden ondernomen.
-
-Vragen om hier te onderzoeken zijn:
- * Staat het systeem de betrokkenen toe om verwijdering aan te vragen?
- * Moet het intrekken van de toestemming van de gebruiker leiden tot automatische verwijdering?
- * Zijn er gegevens verzameld zonder toestemming of op onrechtmatige wijze?
- * Voldoen we aan de overheidsvoorschriften voor gegevensprivacy?
-
- #### 2.6 Gegevenssetbias
-
-Dataset of [Verzamel Bias](http://researcharticles.com/index.php/bias-in-data-collection-in-research/) gaat over het selecteren van een _niet-representatieve_ subset van gegevens voor de ontwikkeling van algoritmen, waardoor potentiële oneerlijkheid in resultaatuitkomsten voor diverse groepen ontstaat. Soorten bias zijn onder meer selectie- , vrijwilligers- en instrumentbias.
-
-Vragen om hier te onderzoeken zijn:
- * Hebben we een representatieve set van betrokkenen geworven?
- * Hebben we onze verzamelde of samengestelde dataset getest op verschillende vooroordelen?
- * Kunnen we ontdekte vooroordelen verminderen of verwijderen?
-
-#### 2.7 Gegevenskwaliteit
-
-[Data Quality](https://lakefs.io/data-quality-testing/) kijkt naar de validiteit van de samengestelde dataset die is gebruikt om onze algoritmen te ontwikkelen en controleert of functies en records voldoen aan de vereisten voor het niveau van nauwkeurigheid en consistentie nodig voor ons AI-doel.
-
-Vragen om hier te onderzoeken zijn:
- * Hebben we geldige _features_ vastgelegd voor onze use case?
- * Zijn gegevens _consistent_ vastgelegd in verschillende gegevensbronnen?
- * Is de dataset _compleet_ voor diverse omstandigheden of scenario's?
- * Wordt informatie _nauwkeurig_ vastgelegd in weerspiegeling van de werkelijkheid?
-
-#### 2.8 Algoritme Eerlijkheid
-
-[Algorithm Fairness](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) controleert of het ontwerp van het algoritme systematisch discrimineert tegen specifieke subgroepen van betrokkenen die leiden tot [potentiële schade](https://docs .microsoft.com/en-us/azure/machine-learning/concept-fairness-ml) in _allocation_ (waar middelen worden geweigerd of onthouden aan die groep) en _quality of service_ (waar AI voor sommige subgroepen niet zo nauwkeurig is als het is voor anderen).
-
-Vragen om hier te onderzoeken zijn:
- * Hebben we de modelnauwkeurigheid geëvalueerd voor verschillende subgroepen en condities?
- * Hebben we het systeem onderzocht op mogelijke schade (bijv. stereotypering)?
- * Kunnen we gegevens herzien of modellen omscholen om geïdentificeerde schade te beperken?
-
-Verken bronnen zoals [AI Fairness checklists](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4t6dA) voor meer informatie.
-
-#### 2.9 Verkeerde voorstelling van zaken
-
-[Vertegenwoordigende data](https://www.sciencedirect.com/topics/computer-science/misrepresentation) gaat over de vraag of we inzichten uit eerlijk gerapporteerde gegevens op een misleidende manier communiceren om een ​​gewenst verhaal te ondersteunen.
-
-Vragen om hier te onderzoeken zijn:
- * Rapporteren we onvolledige of onjuiste gegevens?
- * Visualiseren we gegevens op een manier die leidt tot misleidende conclusies?
- * Gebruiken we selectieve statistische technieken om uitkomsten te manipuleren?
- * Zijn er alternatieve verklaringen die tot een andere conclusie kunnen leiden?
-
-#### 2.10 Vrije keuze
-De [Illusie van Vrije Keuze](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) treedt op wanneer systeem-keuze-architecturen besluitvormingsalgoritmen gebruiken om mensen ertoe aan te zetten een gewenste uitkomst te kiezen terwijl het hen opties en controle lijkt te geven. Deze ['darkpatterns'](https://www.darkpatterns.org/) kunnen gebruikers sociale en economische schade toebrengen. Omdat beslissingen van gebruikers van invloed zijn op gedragsprofielen, kunnen deze acties toekomstige keuzes stimuleren die de impact van deze schade kunnen vergroten of uitbreiden.
-
-Vragen om hier te onderzoeken zijn:
- * Begreep de gebruiker de implicaties van het maken van die keuze?
- * Was de gebruiker op de hoogte van (alternatieve) keuzes en de voor- en nadelen van elk?
- * Kan de gebruiker een geautomatiseerde of beïnvloede keuze later terugdraaien?
- ### 3. Casestudy's
-
-Om deze ethische uitdagingen in een reële context te plaatsen, helpt het om casestudies te bekijken die de potentiële schade en gevolgen voor individuen en de samenleving benadrukken, wanneer dergelijke ethische schendingen over het hoofd worden gezien.
-
-Hier zijn een paar voorbeelden:
-
-| Ethische uitdaging | Casestudy |
-|--- |--- |
-| **Informed Consent** | 1972 - [Tuskegee Syphilis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - Afro-Amerikaanse mannen die deelnamen aan het onderzoek kregen gratis medische zorg beloofd _maar werden misleid_ door onderzoekers die de proefpersonen niet informeerden over hun diagnose of over beschikbaarheid van de behandeling. Veel proefpersonen stierven en ook partners en kinderen werden hierdoor getroffen; de studie duurde 40 jaar. |
-| **Gegevensprivacy** | 2007 - De [Netflix-gegevensprijs](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) voorzag onderzoekers van _10 miljoen geanonimiseerde filmranglijsten van 50.000 klanten_ om aanbevelingsalgoritmen te helpen verbeteren. Onderzoekers waren echter in staat om geanonimiseerde gegevens te correleren met persoonlijk identificeerbare gegevens in _externe datasets_ (bijv. IMDb-commentaren) - waardoor sommige Netflix-abonnees effectief te "de-anonimiseren" waren.|
-| **Verzamelingsbias** | 2013 - De stad Boston [ontwikkelde Street Bump](https://www.boston.gov/transportation/street-bump), een app waarmee burgers kuilen kunnen melden, waardoor de stad betere weggegevens krijgt om problemen op te sporen en op te lossen. [mensen in lagere inkomensgroepen hadden echter minder toegang tot auto's en telefoons](https://hbr.org/2013/04/the-hidden-biases-in-big-data), waardoor hun problemen met de rijbaan onzichtbaar werden in deze app . Ontwikkelaars werkten samen met academici aan _gelijke toegang en digitale scheidslijnen_ kwesties voor eerlijkheid. |
-| **Algoritmische eerlijkheid** | 2018 - De MIT [Gender Shades Study](http://gendershades.org/overview.html) evalueerde de nauwkeurigheid van AI-producten voor genderclassificatie, waarbij hiaten in de nauwkeurigheid voor vrouwen en personen van kleur werden blootgelegd. Een [Apple Card uit 2019](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) leek vrouwen minder krediet te bieden dan mannen. Beide illustreerden problemen in algoritmische bias die tot sociaaleconomische schade leiden.|
-| **Onjuiste voorstelling van gegevens** | 2020 - Het [Departement van Volksgezondheid van Georgië heeft COVID-19-kaarten vrijgegeven](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid- 19-gevallen-afnemende-heropening) die burgers leek te misleiden over trends in bevestigde gevallen met niet-chronologische volgorde op de x-as. Dit illustreert een verkeerde voorstelling van zaken door middel van visualisatietrucs. |
-| **Illusie van vrije keuze** | 2020 - Leerapp [ABCmouse betaalde $ 10 miljoen om een ​​FTC-klacht op te lossen](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) waar ouders aan een abonnementen vastzaten die ze niet konden opzeggen. Dit illustreert duistere patronen in keuzearchitecturen, waarbij gebruikers naar potentieel schadelijke keuzes werden gepusht. |
-| **Gegevensprivacy en gebruikersrechten** | 2021 - Facebook [Data Breach](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify- gebruikers) gegevens van 530 miljoen gebruikers werden openbaar gemaakt, wat resulteerde in een schikking van $ 5 miljard aan de FTC. De organisatie weigerde echter gebruikers op de hoogte te stellen van de inbreuk die de gebruikersrechten rond gegevenstransparantie en -toegang schendt. |
-
-Meer casestudy's bekijken? Bekijk deze bronnen:
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - ethische dilemma's in verschillende sectoren.
-* [cursus Data Science Ethics](https://www.coursera.org/learn/data-science-ethics#syllabus) - baanbrekende casestudies onderzocht.
-* [Waar het mis is gegaan](https://deon.drivendata.org/examples/) - deon checklist met voorbeelden
-
-> 🚨 Denk eens aan de casestudies die u hebt gezien - heeft u in uw leven een soortgelijke ethische uitdaging meegemaakt of erdoor getroffen? Kun je ten minste één andere casestudy bedenken die een van de ethische uitdagingen illustreert die we in deze sectie hebben besproken?
-
-## Toegepaste ethiek
-
-We hebben gesproken over ethische concepten, uitdagingen en casestudy's in reële contexten. Maar hoe beginnen we met het _toepassen_ van ethische principes en praktijken in onze projecten? En hoe _operationaliseren_ we deze praktijken voor beter bestuur? Laten we enkele echte oplossingen verkennen:
-
-### 1. Professionele codes
-
-Beroepscodes bieden organisaties één mogelijkheid om leden te "stimuleren" om hun ethische principes en missieverklaring te ondersteunen. Codes zijn _morele richtlijnen_ voor professioneel gedrag en helpen werknemers of leden om beslissingen te nemen die in lijn zijn met de principes van hun organisatie. Ze zijn slechts zo goed als de vrijwillige medewerking van leden; veel organisaties bieden echter extra beloningen en boetes om naleving door leden te motiveren.
-
-Voorbeelden zijn:
-
- * [Oxford München](http://www.code-of-ethics.org/code-of-conduct/) Ethische code
- * [Data Science Association](http://datascienceassn.org/code-of-conduct.html) Gedragscode (gemaakt in 2013)
- * [ACM-code voor ethiek en professioneel gedrag](https://www.acm.org/code-of-ethics) (sinds 1993)
-
-> 🚨 Behoor jij tot een professionele engineering- of datawetenschapsorganisatie? Verken hun site om te zien of ze een professionele ethische code definiëren. Wat zegt dit over hun ethische principes? Hoe "stimuleren" ze leden om de code te volgen?
-
-### 2. Ethische checklists
-
-Hoewel professionele richtlijnen _ethisch gedrag_ van beoefenaars definiëren, hebben ze [bekende beperkingen](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) bij de handhaving, met name bij grootschalige projecten. In plaats daarvan pleiten veel data Science-experts [voor checklists](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md), die **principes kunnen verbinden met praktijken** in meer deterministische en bruikbare manieren.
-
-Checklists zetten vragen om in "ja/nee"-taken die kunnen worden geoperationaliseerd, zodat ze kunnen worden gevolgd als onderdeel van standaard workflows voor productreleases.
-
-Voorbeelden zijn:
- * [Deon](https://deon.drivendata.org/) - een checklist voor gegevensethiek voor algemene doeleinden gemaakt op basis van [aanbevelingen voor de sector](https://deon.drivendata.org/#checklist-citations) met een opdracht- line tool voor eenvoudige integratie.
- * [Privacy Audit Checklist](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - biedt algemene richtlijnen voor informatieverwerkingspraktijken vanuit juridische en sociale blootstellingsperspectieven.
- * [AI Fairness Checklist](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) - gemaakt door AI-beoefenaars ter ondersteuning van de invoering en integratie van eerlijkheidscontroles in AI-ontwikkelingscycli .
- * [22 vragen voor ethiek in data en AI](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - meer open kader, gestructureerd voor de eerste verkenning van ethische kwesties in ontwerp-, implementatie- en organisatorische contexten.
-
- ### 3. Ethische voorschriften
-
-Ethiek gaat over het definiëren van gedeelde waarden en het _vrijwillig_ doen van de juiste dingen. **Compliance** gaat over het _volgen van de wet_ indien en waar gedefinieerd. **Governance** omvat in grote lijnen alle manieren waarop organisaties handelen om ethische principes af te dwingen en te voldoen aan gevestigde wetten.
-
-Tegenwoordig neemt governance binnen organisaties twee vormen aan. Ten eerste gaat het om het definiëren van **ethische AI**-principes en het vaststellen van praktijken om de acceptatie in alle AI-gerelateerde projecten in de organisatie te operationaliseren. Ten tweede gaat het om het naleven van alle door de overheid opgelegde **gegevensbeschermingsvoorschriften** voor de regio's waarin het actief is.
-
-Voorbeelden van gegevensbescherming en privacyregelgeving:
-
- * `1974`, [US Privacy Act](https://www.justice.gov/opcl/privacy-act-1974) - regelt de verzameling, het gebruik en de openbaarmaking van persoonlijke informatie door de federale overheid.
- * `1996`, [US Health Insurance Portability & Accountability Act (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html) - beschermt persoonlijke gezondheidsgegevens.
- * `1998`, [Amerikaanse Children's Online Privacy Protection Act (COPPA)](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule) - beschermt de gegevensprivacy van kinderen onder de 13 jaar.
- * `2018`, [Algemene Verordening Gegevensbescherming (AVG)](https://gdpr-info.eu/) - biedt gebruikersrechten, gegevensbescherming en privacy.
- * `2018`, [California Consumer Privacy Act (CCPA)](https://www.oag.ca.gov/privacy/ccpa) geeft consumenten meer _rechten_ over hun (persoonlijke) gegevens.
- * `2021`, China's [wet ter bescherming van persoonsgegevens](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021 -08-20/) zojuist gepasseerd, waardoor een van de sterkste online gegevensprivacyregels ter wereld is gecreëerd.
-
-> 🚨 De door de Europese Unie gedefinieerde AVG (Algemene Verordening Gegevensbescherming) blijft vandaag een van de meest invloedrijke regels voor gegevensprivacy. Wist u dat het ook [8 gebruikersrechten](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr) definieert om de digitale privacy en persoonlijke gegevens van burgers te beschermen? Lees wat deze zijn en waarom ze belangrijk zijn.
-
-
-### 4. Ethische cultuur
-
-Onthoud dat er een ongrijpbare kloof blijft tussen _compliance_ (genoeg doen om te voldoen aan "de letter van de wet") en het aanpakken van [systeemproblemen](https://www.coursera.org/learn/data-science-ethics/home/week /4) (zoals ossificatie, informatieasymmetrie en oneerlijke verdeling) die de bewapening van AI kunnen versnellen.
-
-Dit laatste vereist [samenwerkingsbenaderingen voor het definiëren van ethische culturen](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) die emotionele verbindingen en consistente gedeelde waarden _over organisaties_ in de industrie. Dit vraagt ​​om meer [geformaliseerde data-ethiekculturen](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) in organisaties - waardoor _iedereen_ [aan het Andon-koord kan trekken](https:/ /en.wikipedia.org/wiki/Andon_(manufacturing)) (om ethische problemen vroeg in het proces aan de orde te stellen) en het maken van _ethische beoordelingen_ (bijvoorbeeld bij het aannemen) een kerncriterium voor teamvorming in AI-projecten.
-
----
-## [Quiz voor na het college](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/3) 🎯
-## Review & Zelfstudie
-
-Cursussen en boeken helpen bij het begrijpen van kernconcepten en uitdagingen op het gebied van ethiek, terwijl casestudy's en hulpmiddelen helpen bij toegepaste ethische praktijken in echte contexten. Hier zijn een paar bronnen om mee te beginnen.
-
-* [Machine Learning voor beginners](https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md) - les over eerlijkheid, van Microsoft.
-* [Principles of Responsible AI](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/) - gratis leertraject van Microsoft Learn.
-* [Ethiek en gegevenswetenschap](https://resources.oreilly.com/examples/0636920203964) - O'Reilly EBook (M. Loukides, H. Mason et. al)
-* [Data Science Ethics](https://www.coursera.org/learn/data-science-ethics#syllabus) - online cursus van de Universiteit van Michigan.
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - casestudy's van de Universiteit van Texas.
-
-# Opdracht
-
-[Schrijf een data-ethiek case study](/assignment.nl.md)
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-# Introdução a Ética de Dados
-
-|![ Sketchnote por [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/02-Ethics.png)|
-|:---:|
-| Ética em Ciência de Dados - _Sketchnote por [@nitya](https://twitter.com/nitya)_ |
-
----
-
-Nós somos todos cidadãos dos dados vivendo em um mundo de dados.
-
-Tendências do mercado nos mostram que até 2022, 1 em 3 grandes organizações irá comprar e vender seus dados através de [Marketplaces e Exchanges](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/) online. Como **Desenvolvedores de Aplicativos**, nós vamos achar mais fácil e mais barato integrar insights baseados em dados e automações baseadas em algoritmos nas experiências diárias dos usuário. Mas conforme IA se torna mais difundida, nós também vamos precisar entender os danos potenciais causado pelo uso desses algoritmos [como uma arma](https://www.youtube.com/watch?v=TQHs8SA1qpk).
-
-Tendências também indicam que nós vamos criar e consumir mais de [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) de dados em 2025. Como **Cientistas de Dados**, isso nos dará níveis de acesso sem precedentes à dados pessoais. Isso significa que poderemos construir perfis comportamentais dos usuário e influenciar tomadas de decisão de uma forma que crie a [ilusão da livre escolha](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) enquanto potencialmente direcionando os usuários na direção do resultado que nós preferimos. Isso também levanta questões mais amplas sobre privacidade dos dados e proteção dos usuários.
-
-Ética dos dados é agora uma _proteção necessário_ para ciẽncia de dados e engenharia, nos ajudando a minimizar potenciais danos e consequências não intencionas das nossas ações realizadas com base em dados. O [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifica tendências relevantes ná ética digital, IAs responsáveis, e governanças de IA como principais impulsionadores para grandes mega tendências sobre _democratização_ e _industrialização_ da IA.
-
-![Gartner's Hype Cycle for AI - 2020](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
-
-Nessa aula, nós vamos explorar a área fascinante de ética dos dados - desde conceitos essenciais e desafios, para estudos de caso e conceitos de IA aplicados como governança - isso ajuda a estabelecer a cultura da ética nos times e organizações que trabalham com dados e IA.
-
-
-
-
-## [Quiz pré aula](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/2) 🎯
-
-## Definição Básica
-
-Vamos começar entendendo o básico da terminologia.
-
-A palavra "ética" vem da [palavra Grega "ethikos"](https://en.wikipedia.org/wiki/Ethics) (e sua raíz "ethos") que significa _caráter ou natureza moral_.
-
-**Ética** é sobre os valores e princípios morais compartilhados que governam o nosso comportamento em sociedade. Ética é baseada não nas leis mas nas normas amplamente aceitas sobre o que é "certo vs. errado". No entanto, considerações éticas podem influenciar iniciativas de governança corporativa e regulamentações governamentais que criam mais incentivos para conformidade (compliance).
-
-**Ética de Dados** é uma [nova ramificação da ética](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1) que "estuda e avalia problemas morais relacionados a _dados, algoritmos e práticas correspondentes_". Aqui, **"dados"** focam nas ações relacionadas a geração, gravação, curadoria, disseminação de processamento, compartilhamento, e uso, **"algoritmos"** focam em IA, agentes, aprendizado de máquina, e robôs, e **"práticas"** focam em tópicos como inovação responsável, programação, hacking e códigos de ética.
-
-**Ética Aplicada** é a [aplicação prática de considerações morais](https://en.wikipedia.org/wiki/Applied_ethics). É o processo de investigar ativamente problemáticas éticas no contexto de _ações do mundo real, produtos e processos_, e tomar medidas corretivas para fazer com que esses permanecam alianhados com o nossos valores éticos definidos.
-
-**Cultura Ética** é sobre [operacionalizar a ética aplicada](https://hbr.org/2019/05/how-to-design-an-ethical-organization) para garantir que nossos princípios e práticas éticas sejam adotados de maneira consistente e escalável em toda a organização. Culturas éticas de sucesso definem princípios éticos em toda a organização, fornecem incentivos significativos para consistência, e reinforça as normas éticas encorajando e amplificando comportmentos desejados em todos os níveis da organização.
-
-
-## Conceitos Éticos
-
-Nessa seção, nós vamos discutir conceitos como **valores compartilhados** (princípios) e **desafios éticos** (problemas) para a ética de dados - e explorar **estudos de caso** que ajudam você a entender esses conceitos em contextos do mundo real.
-
-### 1. Princípios Éticos
-
-Toda estratégia de ética de dados começa definindo _pricípios éticos_ - os "valores compartilhados" que descrevem comportamentos aceitáveis, e guia ações complacentes, nos nossos dados e nos projetos de IA. Você pode definir eles individualmente ou com um time. No entando, a maioria das grandes organizações descreve eles em uma declaração de missão ou de estrutura de _IA ética_ que é definida em níveis corporativos e aplicadas consistentemente em todos os times.
-
-**Exemplo:** a declaração de missão da [IA responsável](https://www.microsoft.com/pt-br/ai/responsible-ai?activetab=pivot1:primaryr6) da Microsoft afirma: _"Estamos comprometidos com o avanço da AI impulsionados por princípios éticos que colocam as pessoas em primeiro lugar."_ - identificando 6 princípios éticos na estrutura abaixo:
-
-![IA Responśavel na Microsoft](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png)
-
-Vamos explorar brevemente esses princípios. _Transparência_ e _responsabilidade_ são valores fundamentais nos quais outros princípios construíram sobre - então vamos começar aí:
-
-* [**Responsabilidade**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) torna os profissionais _responsáveis_ pelos seus dados e operações da IA, e conformidade (compliance) com esses princípios éticos.
-* [**Transparência**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) garante que os dados e as ações da IA são _compreesíveis_ (interpretáveis) para os usuários, explicando o que e o porquê por trás de cada decisão.
-* [**Justiça**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) - foca em garantir que a IA _trate_ todas as pessoas de forma justa, abordando quaisquer preconceitos sociotécnicos implícitos ou sistêmicos nos dados e sistemas.
-* [**Confiabilidade e Segurança**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - garante que a IA comporte de maneira _consistente_ com os valores definidos, minimizando potenciais danos ou consequências não pretendidas.
-* [**Segurança e Privacidade**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - é sobre compreender as linhagem dos dados, e fornecer _privacidade de dados e proteções relacionadas_ aos usuários.
-* [**Inclusão**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) - é sobre projetar soluções de IA com intenção, adaptando elas para atender uma _vasta game de necessidades humanas_ & capacidades.
-
-> 🚨 Pense sobre qual poderia ser a frase de missão da sua ética de dados. Explore estruturas éticas de IA de outras organizações - aqui estão alguns exemplos da [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles), e [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/). Quais valores compartilhados vocês tem em comum? Como esses princípios se relacionam ao produto de IA ou à indústria na qual eles operam?
-
-### 2. Desafios de Ética
-
-Uma vez que nossos princípios éticos estão definidos, o próximo passo é avaliar nossos dados e ações da IA  para ver se eles estão alinhados com aqueles valores compartilhados. Pense sobre suas ações em duas categorias: _coleção de dados_ e _design de algoritmo_.
-
-Com coleções dados, ações irão, provavelmente, envolver **dados pessoais** ou informação pessoalmente identificável (do Inglês, personally identifiable information, ou PII) para indivíduos vivos identificáveis. Isso inclui [itens diversos de dados não pessoais](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en) que _coletivamente_ identificam um indivíduo. Desafios éticos podem estar relacionados à _privacidade dos dados_, _qualidade dos dados_, e tópicos relacionados como _consentimento informado_ e _direitos de propriedades intelectuais_ para os usuários.
-
-Com o design de algoritmo, as ações envolverão coleta e curadoria dos **datasets**, e então o uso deles para treinar e implantar **modelos de dados** que predizem resultados ou automatizam decisões em contextos do mundo real. Desafios éticos podem surgir de _vieses do dataset_ (biases), problemas com a _qualidade de dados_, _injustiça_, e _má representação_ nos algoritmos - incluindo alguns problemas que são sistêmicos na natureza.
-
-Em ambos os casos, desafios de ética destacam áreas onde nossas ações podem conflitar com nossos valores compartilhados. Para detectar, mitigar, minimizar, ou eliminar, essas preocupações - nós precisamos perguntar questões morais de "sim ou não" relacionadas as nossas ações, e então tomar uma ação corretiva conforme necessário. Vamos olhar alguns desafios éticos e as questões morais que eles levantam:
-
-
-#### 2.1 Propriedade de Dados
-
-A coleta de dados geralmente envolve dados pessoais que podem identificar os titulares dos dados. [Propriedade de dados](https://permission.io/blog/data-ownership) é sobre o _controle_ e [_direitos dos usuários_](https://permission.io/blog/data-ownership) relacionados à criação, processamento, e disseminação dos dados.
-
-As questões morais que precisamos nos perguntar são:
- * Quem detêm/possui os dados? (usuário ou organização)
- * Quais direitos os titulares dos dados tem? (ex: acesso, apagar, portabilidade)
- * Quais direitos as organizações tem? (ex: retificar reviews maliciosas de usuários)
-
-#### 2.2 Consentimento Informado
-
-[Consentimento Informado](https://legaldictionary.net/informed-consent/) define o ato dos usuários aceitar uma ação (como a coleta de dados) com um _compreendimento total_ de fatos relevantes incluindo propósito, potenciais riscos, e alternativas.
-
-Questões a se explorar aqui são:
- * O usuário (titular dos dados) deu permissão para a captação e uso dos dados?
- * O usuário entendeu o propósito para o qual aqueles dados foram coletados?
- * O usuário entendeu os potenciais riscos de sua participação?
-
-#### 2.3 Propriedade Intelectual
-
-[Propriedade intelectual](https://en.wikipedia.org/wiki/Intellectual_property) se refere a criações intangíveis que foram resultados das iniciativas humanas, que podem _ter valor econômico_ para indivíduos ou negócios.
-
-Questões a se explorar aqui são:
- * Os dados coletados tem valor econômicos para um usuário ou negócio?
- * O **usuário** tem propriedade intelectual aqui?
- * As **organizações** tem propriedade intelectual aqui?
- * Se esses direitos existem, como estamos protejendo eles?
-
-#### 2.4 Privacidade de Dados
-
-[Privacidade de dados](https://www.northeastern.edu/graduate/blog/what-is-data-privacy/) ou privacidade da informação se refere a preservação da privacidade do usuário e proteção da identidade do usuário com relação as informações de indentificação pessoal.
-
-Questões a se explorar aqui são:
- * Os dados (pessoais) dos usuários estão protegidos contra hacks e vazamentos?
- * Os dados do usuário são acessíveis somente a usuários e contextos autorizados?
- * A anonimidade do usuário são preservados quando os dados são compartilhados ou disseminados?
- * Um usuário podem ser desindentificado de datasets anônimos?
-
-
-#### 2.5 Direito a Ser Esquecido
-
-o [Direito a Ser Esquecido](https://en.wikipedia.org/wiki/Right_to_be_forgotten) ou [Direito de Apagar](https://www.gdpreu.org/right-to-be-forgotten/) fornecem proteções de dados adicionais para os usuários. Especificamente, dá aos usuários o direito de pedir deleção ou remoção dos dados pessoais das buscas da Internet e outros locais, _sobre circunstâncias específicas_ - permitindo a eles um novo começo online sem que as ações passadas sejam colocadas contra eles.
-
-Questões a se explorar aqui são:
- * O sistema permite que os titulares dos dados peçam o apagamento dos mesmos?
- * A retirada do consentimento do usuário deve acionar um apagamento automático?
- * Dados foram colocados sem o consentimento ou por meios ilegais?
- * Estamos de acordo com regulações governamentais para a privacidade de dados?
-
-
-#### 2.6 Viéses dos Datasets
-
-[Viéses da Coleção ou do Dataset](http://researcharticles.com/index.php/bias-in-data-collection-in-research/) é sobre selecionar um subset de dados _não representativos_ para o desenvolvimento de um algoritmo, criando potenciais injustiças nos resultados para grupos diversos. Os tipos de viéses incluem seleção ou viés da amostra, viés voluntário, e viés do instrumento.
-
-Questões a se explorar aqui são:
- * Recrutamos um conjunto representativo de titulares de dados?
- * Nós testamos nossos datasets colecionados ou com curadoria para diversos viéses?
- * Nós podemos mitigar ou remover quaisquer viéses descobertos?
-
-#### 2.7 Qualidade de Dados
-
-[Qualidade de Dados](https://lakefs.io/data-quality-testing/) procura pela validade do dataset com curadoria usado para desenvolver nossos algoritmos, checando para ver se recursos e registros atendem os requisitos para o nível de acurácia e consistência necessários para o propósito da nossa IA.
-
-Questões a se explorar aqui são:
- * Nós coletamos _features_ válidos para nosso caso de uso?
- * Os dados foram coletados _consistentemente_ em diversas fontes de dados?
- * O dataset é _completo_ para diversas condições e cenários?
- * As informações capturadas refletem _com precisão_ a realidade?
-
-#### 2.8 Justiça do Algoritmo
-
-[Justiça do Algoritmo](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) checa para ver se o design do algoritmo discrimina sistematicamente subgrupos específicos dos titulares dos dados levando a [potenciais danos](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml) em _alocação_ (onde recursos são negados ou detidos daquele grupo) e _qualidade de serviço_ (onde IA não é tão acurada para alguns subgrupos quanto é para outros).
-
-Questões a se explorar aqui são:
- * Nós avaliamos a acurácia do modelo para diversos subgrupos e condições?
- * Nós examinamos o sistema em busca de danos potenciais (ex. estereótipos)?
- * Nós podemos revisar os dados ou retreinar os modelos para mitigar danos identificados?
-
-Explore recursos como [Checklist de Justiça de IA](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4t6dA) para saber mais.
-
-#### 2.9 Má Representação
-
-[Má Representação dos Dados](https://www.sciencedirect.com/topics/computer-science/misrepresentation) é sobre perguntar se nós estamos comunicando insights de dados honestamente relatados de uma maneira enganosa para suportar uma narrativa desejada.
-
-Questões a se explorar aqui são:
- * Estamos relatando dados completos ou inacurados?
- * Estamos visualizando dados de uma maneira que conduz a uma conclusão errada?
- * Estamos usando técnicas estatísticas seletivas para manipular os resultados?
- * Existem explicações alternativas que podem oferecer uma conclusão diferente?
-
-#### 2.10 Livre Escolha
-A [Ilusão da Livre Escolha](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) ocorre quando as "arquiteturas de escolha" do sistema utiliza algoritmos de tomada de decisão para incentivar as pessoas a obterem um resultado preferido enquanto parece lhe dar opções e controle. Esses [dark patterns](https://www.darkpatterns.org/) podem causar danos sociais e econômicos aos usuários. Já que as decisões do usuário impactam perfis de comportamento, essas ações potencialmente conduzem as escolhas futuras que podem aplificar ou extender o impacto desses danos.
-
-Questões a se explorar aqui são:
- * O usuário entende as implicações de fazer aquela escolha?
- * O usuário estava ciente das escolhas (alternativas) e dos prós e contras de cada uma?
- * O usuário pode reverter um escolha automatizada ou influenciada depois?
-
-### 3. Estudo de Casos
-
-Para colocar esses desafios éticos em contextos do mundo real, ajuda olhar para estudo de casos que destacam potenciais danos e consequências para indivíduos e sociedade, quando essas violações éticas são negligenciadas.
-
-Aqui estão alguns exemplos:
-
-| Desafios de Éticas | Estudo de Caso  | 
-|--- |--- |
-| **Consentimento Informado** | 1972 - [Tuskegee Syphillis Study](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - Homens afro-americanos que participaram no estudo foram prometidos cuidados médicos livres de custo _mas foram enganados_ pelos pesquisadores que não informaram os participantes de seus diagnósticos ou sobre a avaliabilidade de tratamentos. Muitos participantes morreram e parceiros e ciranças foram afetados; oe studo durou por 40 anos. | 
-| **Privacidade de Dados** |  2007 - O [Netflix data prize](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) forneceu a pesquisadores _10M de avaliações anônimas de filmes de 50K clientes_ para ajudar a melhorar os algoritmos de recomendação. No entanto, os pesquisadores conseguiram correlacionar os dados anônimos com dados de identificação pessoal em _datasets externos_ (ex. comentários no IMDb) - "desanonimizando" efetivamente alguns assinates da Netflix.|
-| **Viéses dos Datasets**  | 2013 - A Cidade de Boston [desenvolveu Street Bump](https://www.boston.gov/transportation/street-bump), um aplicativo que deixa os usuários relatarem burcos nas ruas, dando à cidade melhores dados rodoviários para encontrar e consertar problemas. No entanto, [pessoas que faziam parte de grupos de baixa renda tinham menos acesso a carros e celulares](https://hbr.org/2013/04/the-hidden-biases-in-big-data), fazendo com que os seus problema rodoviários fossem invisíveis nesse aplicativo. Desenvolvedores trabalharm com acadêmicos para questões de _acesso equitativo e divisões digitais_ para justiça. |
-| **Justiça do Algoritmo**  | 2018 - [O Gender Shades Study do MIT](http://gendershades.org/overview.html) avaliou a acurácia de produtos de IA de classificação de gêneros, expondo lacunas na acurácia para mulheres e pessoas não brancas. Um [Apple Card de 2019](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) parece oferecer menos créditos para mulheres do que oferece para homens. Ambos ilustraram questões de viés algorítmico levando a danos socioeconômicos.|
-| **Má Representação de Dados** | 2020 - O [Departamento de Sáude Pública da Georgia (Georgia Department of Public Health) liberou gráficos da COVID-19](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening) que aparentam a levar os cidadãos a conclusões errôneas sobre as tendências em casos confirmados em uma ordem não cronológica no eixo x. Isso ilustra a má representação atráves de truques de visualização. |
-| **Ilusão da Livre Escolha** | 2020 - Aplicativo de aprendizado [ABCmouse pagou $10M para resolver uma reclamação do FTC](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/) onde os pais foram enganados a pagar assinaturas que eles não podiam cancelar. Isso ilustra "dark patterns" em arquiteturas de escolha, onde usuários foram direcionados a escolhas potencialmente prejudiciais. |
-| **Privacidade de Dados & Direitos do Usuário** | 2021 - [Violação de Dados do facebook](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) expôs dados de mais de 530M de usuários, resultando em um acordo de $5B com o FTC (Federal Trade Commission). No entanto, o Facebook se recusou a notificar os usuários sobre a violação dos dados violando os direitos dos usuários de transparência e acesso de dados. |
-
-Gostaria de explorar mais estudos de caso? Confira:
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - dilemas éticos em indústrias diversas.
-* [Data Science Ethics course](https://www.coursera.org/learn/data-science-ethics#syllabus) - estudos de caso marcantes explorados.
-* [Where things have gone wrong](https://deon.drivendata.org/examples/) - checklists da deon com exemplos
-
-> 🚨 Pense sobre estudos de caso que você ja viu - você ja experienciou, ou foi afetado por, um desafio ético similar em sua vida? Voce consegue pensar em pelo menos um estudo de caso que ilustre um ou mais desafios éticos que discutimos nessa seção?
-
-## Ética aplicada
-
-Nós falamos sobre conceitos de éticas, desafios, e casos de estudo em contextos do mundo real. Mas como nós começamos a _aplicar_ esses princípios éticos em nossos projetos? E como nós _operacionalizamos_ essas práticas para melhor governância? Vamos explorar algumas soluções do mundo real:
-
-### 1. Códigos Profissionais
-
-Códigos Profisionais oferecem uma opção para organizações para "incentivar" membros a apoiar os princípios éticos e frase de missão. Códigos são _diretrizes morais_ para comportamento profissional, ajudando funcionários ou membros a tomar decisões que alinhem com os princípios da sua organização. Eles são tão bons quanto a conformidade voluntária dos membros; no entanto, muitas organizações oferecem recompensas e penalidades adicionais para motivar a conformidade dos membros.
-
-Exemplos incluem:
-
- * [Oxford Munich](http://www.code-of-ethics.org/code-of-conduct/) Código de Ética
- * [Data Science Association](http://datascienceassn.org/code-of-conduct.html) Código de Conduta (criado em 2013)
- * [ACM Code of Ethics and Professional Conduct](https://www.acm.org/code-of-ethics) (desde 1993)
-
-> 🚨 Você faz parte de uma organização profissional de engenharia ou de ciências de dados? Explore o site deles para ver se eles definem um código de ética profissional. O que diz sobre os princípios éticos deles? Como eles estão "incentivando" os membros a seguir o código?
-
-### 2. Checklists de Éticas
-
-Enquanto códigos profissionais definem _comportamentos ético_ requiridos de seus praticantes, eles [tem limitações conhecidas](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md) na execução, particularmente em projetos de larga escala. Ao invés disso, muitos experts em Ciência de Dados [defendem as checklists](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md), que podem **conectar princípios a práticas** de maneiras para determinísticas e acionáveis.
-
-Checklists convertem as questões em tarefas de "sim/não" que podem ser operacionalizadas, permitindo eles serem rastreados como parte dos fluxos de trabalho de liberação de produtos padrão. 
-
-Exemplos incluem:
- * [Deon](https://deon.drivendata.org/) - uma checklist de propósito gerak criado a partir de [recomendações da insústria](https://deon.drivendata.org/#checklist-citations) com uma ferramenta de linha de comando para fácil integração.
- * [Privacy Audit Checklist](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - fornece orientação geral para práticas de manipulação de informação a partir de perspectivas de exposição legal e social.
- * [AI Fairness Checklist](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) - criado por praticantes de IA para apoiar a adoção e integração de verificações de justiça dentro dos ciclos de desenvolvimento de IA.
- * [22 questions for ethics in data and AI](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - estrutura mais aberto-fechado, estrturado para exploração inicial de problemas éticos em contextos de design, implementação, e organizacional.
-
-### 3. Regulações Éticas
-
-Ética é sobre definir valores compartilhados e fazer a coisa certa _voluntariamente_. **Compliance (Conformidade)** é sobre _seguir a lei_ se e onde definida. **Governância** abrange amplamente todos as formas de como as organizações operam para garantir princípios éticos e cumprir as leis estabelecidas.
-
-Hoje, governância assume duas formas dentro das organizações. Primeira, é sobre definir princípios de **IA ética** e estabelecer práticas para operacionalizar a adoção em todos os projetos de IA na organização. Segundo, trata-se de cumprir com todos os **regulamentos de proteção de dados** para as regiões em que operam.
-
-Exemplos de proteção de dados e regulamentos de privacidade:
-
- * `1974`, [US Privacy Act](https://www.justice.gov/opcl/privacy-act-1974) - regula a coleta, o uso, e divulgação de informações pessoais por parte do _governo federal_.
- * `1996`, [US Health Insurance Portability & Accountability Act (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html) - protege dados de sáude pessoais.
- * `1998`, [US Children's Online Privacy Protection Act (COPPA)](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule) - protege a privacidade de dados de crianças menores de 13 anos de idade.
- * `2018`, [General Data Protection Regulation (GDPR)](https://gdpr-info.eu/) - fornece direitos aos usuário, proteção de dados, e privacidade.
- * `2018`, [California Consumer Privacy Act (CCPA)](https://www.oag.ca.gov/privacy/ccpa) dá aos consumidores mais _direitos_ sobre seus dados (pessoais).
- * `2021`, [A Lei de Proteção de Informação Pessoal](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/) da China acabou de ser passado, criando uma das regulações de privacidade de dados online mais forte do mundo.
-
-> 🚨 A GDPR (General Data Protection Regulation) da União Europia continua sendo umas das regulações de privacidade de dados mais influentes hoje em dia. Você sabia que a mesma também define [8 direitos dos usuário](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr) para proteger a privacidade dos cidadãos e dados pessoais? Saiba mais sobre o que são e porque eles importam.
-
-
-### 4. Cultura Ética
-
-Note que existe uma lacuna intangível entre _compliance_ (fazer o suficiente para cumprir a "a carta da lei") e abordar [problemas sistêmicos](https://www.coursera.org/learn/data-science-ethics/home/week/4) (como ossificação, assimetria informacional, e injustiça distribucional) que podem acelerar o uso da IA como uma arma.
-
-Este último requere [abordagens colaborativas para definir culturas éticas](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) que constrói conexões emocionais e valores compartilhados consistentes _em todas as organizações_ na indústria. Isso requere mais [culturas de ética de dados formalizadas](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) nas organizações - permitindo _qualquer um_ a [puxar o cordão Andom](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (para aumentar as preocupações éticas mais cedo no processo) e fazendo _avaliações éticas_ (ex. na contratação) um critério fundamental na formação de times em projetos de IA.
-
----
-## [Quiz pós aula](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/3) 🎯
-## Revisão e Autoestudo
-
-Cursos e livros ajudam a entender os conceitos essencias da ética, enquanto estudos de caso e ferramentas ajudam com práticas da ética aplicado em contextos do mundo real. Aqui estão alguns recursos para começar.
-
-* [Machine Learning For Beginners](https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md) - aula sobre Justiça, da Microsoft.
-* [Principles of Responsible AI](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/) - programa de aprendizado gratuito da Microsoft Learn.
-* [Ethics and Data Science](https://resources.oreilly.com/examples/0636920203964) - O'Reilly EBook (M. Loukides, H. Mason et. al)
-* [Data Science Ethics](https://www.coursera.org/learn/data-science-ethics#syllabus) - curso online da Universidade de Michigan.
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - estudos de caso da Universidade do Texas.
-
-# Tarefa 
-
-[Escreva um Caso de Uso de Ética de Dados](assignment.pt-br.md)
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-# Введение в этику данных
-
-|![ Рисунок [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/02-Ethics.png)|
-|:---:|
-| Этика в науке о данных - _Рисунок [@nitya](https://twitter.com/nitya)_ |
-
----
-
-Мы все инфо-граждане, живущие в инфо-мире.
-
-Из тенденций рынка следует, что к 2022 году одна из трёх крупных организаций будет покупать и продавать свои данные на онлайн [маркетплейсах и биржах](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/). Как **разработчики приложений**, мы интегрируем инсайты на основе данных и алгоритмы автоматизации в ежедневную жизнь пользователя более простым и дешёвым способом. Однако по мере распространения ИИ, нам необходимо осознавать потенциальную угрозу использования подобных алгоритмов в качестве [оружия](https://www.youtube.com/watch?v=TQHs8SA1qpk) в больших масштабах.
-
-Тенденции также показывают, что мы создадим и потребим более [180 зеттабайт](https://www.statista.com/statistics/871513/worldwide-data-created/) данных к 2025 году. Нам, как **дата сайентистам - специалистам по данным**, открывается беспрецедентный уровень доступа к личным данным. Это означает, что мы сможем составлять поведенческие профили пользователей и влиять на принятие решений, создавая [иллюзию свободного выбора](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) и подталкивая пользователей делать то, что нам нужно. Также возникают широкие вопросы о приватности данных и защите пользователей.
-
-В настоящее время, этика данных является _необходимым ограждением_ для специалистов и инженеров по данным, минимизирующим ущерб и непреднамеренные последствия от наших действий на основе данных. На [графике цикла популярности Gartner для ИИ](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) современные тренды в цифровой этике, ответственном ИИ и управлении ИИ обозначены как ключевые двигатели мегатрендов _демократизации_ и _индустриализации_ искусственного интеллекта.
-
-![Цикл популярности Gartner для ИИ - 2020 год](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
-
-В данном уроке мы исследуем увлекательную область этики данных, от основных положений и проблем, до реальных примеров и прикладных концепции, таких как управление данными, которые помогают установить этическую культуру в командах и организациях, работающих с данными и ИИ.
-
-
-
-
-## [Вступительный тест](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/2) 🎯
-
-## Основные определения
-
-Давайте начнём с базовых терминов.
-
-Само слово "этика" происходит от [греческого слова "этикос"](https://ru.wikipedia.org/wiki/%D0%AD%D1%82%D0%B8%D0%BA%D0%B0) (и его корня "этос"), означающего _характер или моральная природа_. 
-
-**Этика** изучает общие ценности и моральные принципы, которые определяют наше поведение в обществе. Этика основывается не на законах, а на широких принятых нормах того, что хорошо, а что плохо. Однако, этические соображения могут влиять на политику компаний и государственное регулирование, которое создает больше стимулов для соблюдения этических норм.
-
-
-**Этика данных** - это [новая ветвь этики](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1), которая "изучает и оценивает моральные вопросы связанные с _данными, алгоритмами и соответствующими практиками_". Упомянутые здесь вопросы **"данных"** сосредоточены вокруг действий генерации, записи, курирования, обработки, распространения, предоставление доступа и использования. Вопросы **"алгоритмов"** сосредоточены вокруг ИИ, агентов, машинного обучения и роботов. Вопросы **"практик"** сфокусированы на темах ответственных инноваций, программирования, хакинга и этичного исходного кода.
-
-**Прикладная этика** - это [практическое применение моральных соображений](https://ru.wikipedia.org/wiki/%D0%9F%D1%80%D0%B8%D0%BA%D0%BB%D0%B0%D0%B4%D0%BD%D0%B0%D1%8F_%D1%8D%D1%82%D0%B8%D0%BA%D0%B0). Это процесс активного исследования этических проблем в контексте _действий, товаров и процессов реального мира_, и принятия корректирующих мер по удержанию их в соответствии нашим принятым этическим ценностям.
-
-**Этическая культура** - раздел об [_использовании_ прикладной этики](https://hbr.org/2019/05/how-to-design-an-ethical-organization) для того, чтобы убедиться, что наши этические принципы и порядки приняты полномасштабно в рамках всей организации и действуют постоянно. Успешные этические культуры определяют общеорганизационные этические принципы, обеспечивают ощутимые стимулы для их соблюдения и укрепляют этические нормы, поощряя желаемое поведение на каждом уровне организации.
-
-
-## Этические концепции
-
-В данном разделе мы обсудим такие понятия как **общие ценности** (принципы) и **этические вызовы** (проблемы) в этике данных, а также исследуем **реальные примеры**, которые помогут Вам понять эти концепции в реальном мире.
-
-### 1. Принципы этики
-
-Каждая стратегия в этике данных начинается с определения _этических принципов_ - "общих ценностей", которые описывают приемлемое поведение и регламентируют соответствующие действия в проектах, связанных с данными и ИИ. Вы можете определить их на индивидуальном уровне или на уровне команды. Однако, большинство крупных организаций выделяют их как миссию по созданию _этичного ИИ_ или набора правил, который определён на уровне компании и которому подчиняются все без исключения.
-
-**Пример**: Формулировка миссии об [ответственном ИИ](https://www.microsoft.com/en-us/ai/responsible-ai) компании Майкрософт звучит так: _"Мы преданы идее продвижения ИИ на основе этических принципов, которые ставят людей во главу угла"_. Данный лозунг определяет 6 этических принципов, описанных далее.
-
-![Ответсвенный ИИ в компании Майкрософт](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png)
-
-Давайте кратко рассмотрим эти принципы. _Прозрачность_ и _Ответственность_ являются основными, а остальные строятся поверх, поэтому начнем с главных:
-
-* Принцип [**ответственности**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) накладывает ответственность на активных пользователей за использование данных и ИИ и требует согласия с данными этическими принципами.
-* Принцип [**прозрачности**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) позволяет убедиться, что действия с данными и ИИ _понимаемы_ (осознаваемы) пользователями, разъясняя, что кроется за действиями и их последствия.
-* Принцип [**справедливости**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6) фокусируется на равном восприятии искусственным интеллектом _всех людей_ и обращает внимание на системную и скрытую социально-техническую предвзятость в данных и информационных системах.
-* Принцип [**надёжности и сохранности**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) обеспечивает _постоянное_ соответствие поведения ИИ заранее определённым ценностям, минимизируя потенциальный ущерб или неумышленные последствия.
-* Принцип [**приватности и безопасности**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) говорит о необходимости понимания происхождения данных и обеспечивает _приватность данных и их защиту_ для пользователей.
-* Принцип [**инклюзивности**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6) гарантирует создание решений на основе ИИ с целью адаптировать их к _нуждам широкого круга людей с различными возможностями_.
-
-> 🚨 Подумайте о том, какими могут быть формулировки миссий в этике данных. Познакомьтесь с подходами этичного ИИ в других организациях: [IBM](https://www.ibm.com/cloud/learn/ai-ethics), [Google](https://ai.google/principles) и [Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/). Какие общие ценности являются схожими? Как эти принципы связаны с продуктами и отраслями этих компаний?
-
-
-### 2. Проблемы этики
-
-Как только мы определили этические принципы, следующим шагом становится оценить наши действия с данными и ИИ с точки зрения того, как они соотносятся с принятыми общими ценностями. Подумайте над Вашими действиями в разрезе двух категорий: _сбора данных_ и _разработка алгоритма_.
-
-При сборе данных, список действий наверняка будет включать в себя **персональные данные** или персональные идентифицирующие данные для установления личностей. Они состоят из
-[различных неперсональных данных](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en), которые _в совокупности_ определяются как персональные. Этические вызовы здесь могут относиться к _приватности данных_, _владению данными_ и связанным с ними вопросам _информированного согласия_, и _прав на интеллектуальную собственность_ пользователей.
-
-При разработке алгоритма, действия включают в себя сбор и обработку **датасетов** и последующее использование их для тренировки и развёртывания _модели данных_, способной выдавать прогнозы или автоматизировать принятие решений в реальном мире. Этические вызовы возникают из-за _несбалансированного датасета_, _качества данных_, _несправедливости модели_ и _искажения фактов_ в алгоритмах, включая некоторые системные по своей природе вопросы.
-
-В обоих случаях, этические проблемы подсвечивают области, в которых наши действия могут идти в разрез с нашими общими ценностями. Для того чтобы распознать, смягчить, сократить или устранить конфликты, нам необходимо задать моральные "да/нет" вопросы, связанные с нашими действиями, а затем скорректировать наши действия при необходимости. Давайте взглянем на некоторые этические вызовы и моральные вопросы, которые они поднимают:
-
-
-#### 2.1 Право собственности на данные
-
-Сбор данных часто включает в себя сбор персональных данных, которые могут идентифицировать действующих лиц. [Право собственности на данные](https://permission.io/blog/data-ownership) занимается вопросами _контроля_ и [_прав пользователя_](https://permission.io/blog/data-ownership) в области создания, обработки и распространения данных.
-
-Моральные вопросы, которые мы должны задать себе здесь:
-* Кто владеет данными (пользователь или организация)?
-* Какие права имеют действующие лица, имеющие отношение к данным? (напр., доступ, стирание, перенос)
-* Какие права имеет организация? (напр., редактирование злонамеренных пользовательских отзывов)
-
-#### 2.2 Информированное согласие
-
-[Информированное согласие](https://legaldictionary.net/informed-consent/) определяет согласие пользователя на действия (например, сбор данных) с _полным пониманием_ всех фактов, включая цель, потенциальные риски и альтернативы.
-
-Вопросы для дискуссии:
-* Предоставил ли пользователь (действующее лицо) соглашение на сбор и использование данных?
-* Осознал ли пользователь цель сбора данных?
-* Осознал ли пользователь возможные риски от использования данных?
-
-#### 2.3 Интеллектуальная собственность
-
-[Интеллектуальная собственность](https://ru.wikipedia.org/wiki/%D0%98%D0%BD%D1%82%D0%B5%D0%BB%D0%BB%D0%B5%D0%BA%D1%82%D1%83%D0%B0%D0%BB%D1%8C%D0%BD%D0%B0%D1%8F_%D1%81%D0%BE%D0%B1%D1%81%D1%82%D0%B2%D0%B5%D0%BD%D0%BD%D0%BE%D1%81%D1%82%D1%8C) относится к нематериальным творениям, произведенным по инициативе человека, которые могут _иметь экономическую ценность_ для физических или юридических лиц.
-
-Вопросы для дискуссии:
-* Имеют ли собранные данные экономическую ценность для пользователя или компании?
-* Обладает ли **пользователь** интеллектуальной собственностью в данном случае?
-* Обладает ли **компания** интеллектуальной собственностью в данном случае?
-* Если права на собственность существуют, как мы защищаем их?
-
-#### 2.4 Приватность данных
-
-[Приватность данных](https://www.northeastern.edu/graduate/blog/what-is-data-privacy/) или информационная приватность относится к сохранению приватности пользователя и защиты его личности относительно пользовательской идентифицирующей информации.
-
-Вопросы для дискуссии:
-* Защищены ли пользовательские (персональные) данные от взломов и утечек?
-* Доступны ли пользовательские данные только уполномоченным пользователям и окружениям?
-* Сохраняется ли анонимность пользователя при передаче и распространении данных?
-* Может ли пользователь быть идентифицирован из анонимизированного датасета?
-
-
-#### 2.5 Право на забвение
-
-[Право на забвение](https://ru.wikipedia.org/wiki/%D0%9F%D1%80%D0%B0%D0%B2%D0%BE_%D0%BD%D0%B0_%D0%B7%D0%B0%D0%B1%D0%B2%D0%B5%D0%BD%D0%B8%D0%B5) или [Право на стирание](https://www.gdpreu.org/right-to-be-forgotten/) обеспечивает дополнительную защиту данных пользователя. В особенности, данное право предоставляет пользователям возможность удаления персональных данных из поисковых систем сети Интернет и других мест, позволяя, при определённых обстоятельствах, начать онлайн-историю с чистого листа без учёта предыдущих событий.
-
-Вопросы для дискуссии:
-* Позволяет ли рассматриваемая система действующим лицам запрашивать удаление данных?
-* Должен ли отзыв пользовательского соглашения вызывать автоматическое стирание данных?
-* Были ли данные собраны без согласия или незаконными способами?
-* Действуем ли мы согласно государственному регулированию в сфере приватности данных?
-
-
-#### 2.6 Несбалансированный датасет
-
-Проблема [несбалансированного датасета или коллекции данных](http://researcharticles.com/index.php/bias-in-data-collection-in-research/) появляется при использовании _нерепрезентативной_ выборки для разработки алгоритма, создании потенциальной несправедливости в результатах модели для различных групп. Типы искажений включают в себя систематические ошибки, ошибки наблюдателя, погрешности инструментов.
-
-Вопросы для дискуссии:
-* Имеем ли мы дело с репрезентативным набором действующих лиц?
-* Был ли протестирован собранный или созданный датасет на различные искажения?
-* Можем ли мы уменьшить или избавиться от обнаруженных искажений?
-
-
-#### 2.7 Качество данных
-
-[Качество данных](https://lakefs.io/data-quality-testing/) отображает пригодность собранного датасета, используемого для разработки нашего алгоритма, проверяет, удовлетворяют ли признаки и записи требованиям качества и согласованности, необходимым для целей нашего ИИ.
-
-
-Вопросы для дискуссии:
-* Собрали ли мы _пригодные_ признаки для решения нашей задачи?
-* Собраны ли данные _согласованно_ с различных источников?
-* Является ли датасет _полным_ с точки зрения различных условий и сценариев?
-* _Достоверно_ ли отображает собранная информация реальность?
-
-
-#### 2.8 Справедливость алгоритма
-
-[Справедливость алгоритма](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f) указывает, дискриминирует ли созданный алгоритм отдельные группы действующих лиц и ведет ли это к [потенциальному ущербу](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml) в _распределении_ (когда ресурсы недоступны или, наоборот, удерживаются отдельной группой) и в _качестве услуг_ (когда ИИ недостаточно точен для некоторых групп в отличие от других).
-
-Вопросы для дискуссии:
-* Правильно ли мы оценили точность модели для различных групп и условий?
-* Внимательно ли мы изучили созданную систему на предмет потенциального ущерба (например, на стереотипизацию)?
-* Можем ли мы перепроверить данные или перетренировать модель, чтобы сократить выявленный ущерб?
-
-
-Изучите материалы, подобные [контрольному списку справедливости ИИ](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4t6dA), чтобы узнать больше.
-
-#### 2.9 Искажение фактов
-
-[Искажение фактов в данных](https://www.sciencedirect.com/topics/computer-science/misrepresentation) указывает, вводим ли мы в заблуждение инсайтами, чтобы поддержать желаемую точку зрения, несмотря на достоверные собранные данные.
-
-Вопросы для дискуссии:
-* Предоставляем ли мы неполные или неточные данные?
-* Визуализируем ли мы данные таким образом, который ведет к ошибочным выводам?
-* Используем ли мы статистические методы выборочно, чтобы манипулировать результатами?
-* Существуют ли альтернативные суждения, которые могут привести к другим выводам?
-
-
-#### 2.10 Свободный выбор
-
-[Иллюзия свободного выбора](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) появляется, когда система, "проектирующая выбор", использует алгоритмы принятия решений, чтобы подтолкнуть к предподчтительным действиям, и в то же время обманчиво даёт выбор и контроль над ситуацией. Эти [нечестные уловки](https://www.darkpatterns.org/) могут нанести социальный и экономический вред пользователям. Поскольку решения пользователей влияют на поведенческие паттерны, подобные действия могут привести к последующим принятиям решений, которые приумножат или продлят действие нанесённого ущерба.
-
-Вопросы для дискуссии:
-* Понимает ли пользователь последствия принятия того или иного решения?
-* Был ли пользователь осведомлён о (альтернативном) выборе и плюсах и минусах каждого исхода?
-* Может ли пользователь изменить автоматический выбор или выбор, сделанный под влиянием, позднее?
-
-
-### 3. Случаи из реальной практики
-
-В решении упомянутых этических вызовов в контексте реального мира нам поможет взглянуть на случаи, когда потенциальные угрозы и последствия для личности и общества наиболее выражены, когда этические нарушения упущены из виду.
-
-Вот некоторые примеры:
-
-| Этический вызов | Реальный пример  | 
-|--- |--- |
-| **Информированное согласие** | 1972 год - [Исследование сифилиса в Таскиги](https://ru.wikipedia.org/wiki/%D0%98%D1%81%D1%81%D0%BB%D0%B5%D0%B4%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5_%D1%81%D0%B8%D1%84%D0%B8%D0%BB%D0%B8%D1%81%D0%B0_%D0%B2_%D0%A2%D0%B0%D1%81%D0%BA%D0%B8%D0%B3%D0%B8) - Принявшим участие в исследовании афроамериканцам была обещана бесплатная медицинская помощь, однако их _обманули_ исследователи, не предоставив информацию о диагнозе или доступности лекарств. Многие участники умерли, заразив партнёров или детей. Исследование длилось 40 лет. | 
-| **Приватность данных** |  2007 год - В соревновании [от компании Netflix](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/) участникам предложили _10 миллионов анонимизированных оценок фильмов от 50 тысяч клиентов_ для улучшения алгоритма рекомендаций. Однако, участники оказались способны сопоставить анонимизированные данные с персональными данными из _внешнего источника данных_ (например, комментарии на сайте IMDb) и успешно де-анонимизировать некоторых подписчиков Netflix. |
-| **Систематическая ошибка**  | 2013 год - городские власти города Бостона [разработали Street Bump](https://www.boston.gov/transportation/street-bump), приложение, позволяющее гражданам сообщать о дорожных выбоинах, предоставляя властям более качественные данные для поиска и ремонта. Однако, [люди из группы с низким доходом имели ограниченный доступ к машинам и смартфонам](https://hbr.org/2013/04/the-hidden-biases-in-big-data), и их дорожные проблемы не отображались в этом приложении. Разработчики совместно с учеными исправили проблемы _равного доступа и цифрового неравенства_ для большей справедливости. |
-| **Справедливость алгоритма**  | 2018 год  - В [исследовании гендерных оттенков](http://gendershades.org/overview.html) в университете MIT при оценке точности ИИ для задачи гендерной классификации товаров были обнаружены пробелы в точности для женщин и для "цветных" людей. В 2019 году [кредитная программа компании Apple](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/) предлагала меньший кредитный лимит женщинам по сравнению с мужчинами. Оба случая иллюстрируют предвзятость, ведущую к социально-экономическому ущербу. |
-| **Искажение фактов** | 2020 год - [Департамент штата Джорджия выпустил графики заболеваемости COVID-19](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening), которые ввели в заблуждение граждан насчёт трендов подтверждённых случаев своей хронологической неупорядоченностью. Данный пример показывает искажение фактов при помощи визуализации. |
-| **Иллюзия свободного выбора** | 2020 год - Разработчики обучающего приложения [ABCmouse заплатили 10 миллионов долларов для урегулирования жалобы Федеральной торговой комиссии](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/), в которой родители были вынуждены платить за подписку, которую они не могли отменить. Данный случай иллюстрирует тёмную сторону систем с наличием выбора, в которых пользователей подталкивают к потенциально вредному выбору. |
-| **Приватность данных и права пользователя** | 2021 год - [Утечка данных в компании Facebook](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users) содержала данные 530 миллионов пользователей и повлекла штраф в 5 миллиардов долларов от Федеральной торговой комиссии. Компания отказалась предупреждать пользователей об утечке данных, нарушив тем самым права пользователей на прозрачность и приватный доступ. |
-
-Хотите узнать больше случаев из реальной жизни? Познакомьтесь с данными ресурсами:
-* [Этика без прикрас](https://ethicsunwrapped.utexas.edu/case-studies) - этические дилеммы в различных отраслях. 
-* [Курс этики в науке о данных](https://www.coursera.org/learn/data-science-ethics#syllabus) - рассматриваются ключевые примеры из реальной практики.
-* [Место, где что-то пошло не так](https://deon.drivendata.org/examples/) - список примеров от Deon.
-
-> 🚨 Вспомните реальные случаи из своей жизни. Сталкивались ли Вы сами или пострадали от подобных этических вызовов? Можете ли вы вспомнить по крайней мере ещё один случай, который иллюстрирует один из этических вызовов, которые мы обсудили в данном разделе?
-
-## Прикладная этика
-
-Мы рассмотрели этические концепции, вызовы и случаи из реальной жизни. Но как мы можем начать _применять_ этические принципы в наших проектах? И как мы должны _оперировать_ данными принципами для лучшего управления? Давайте рассмотрим некоторые решения проблем из реальной практики.
-
-### 1. Профессиональные нормы поведения
-
-Профессиональные нормы поведения в организации являются способом _стимулирования_ участников для поддержания её этических принципов и целей. Зафиксированные нормы являются _моральным ориентиром_ для поведения на работе, помощи сотрудникам в принятии решений, которые соответствуют принципам их организации. Они имеют силу только при добровольном согласии участников, однако многие организации предлагают дополнительные вознаграждения и штрафы, чтобы мотивировать участников на согласие. 
-
-Примеры:
-
- * Нормы этики в [университете Оксфорда в городе Мюнхен](http://www.code-of-ethics.org/code-of-conduct/)
- * Нормы поведения в [Ассоциации науки о данных](http://datascienceassn.org/code-of-conduct.html) (написаны в 2013 году)
- * Этические и профессиональные нормы в [Ассоциации вычислительной техники](https://www.acm.org/code-of-ethics) (действуют с 1993 года)
-
-> 🚨 Имеете ли Вы отношение к организациям, занимающимся разработкой ПО и наукой о данных? Исследуйте их сайты на наличие зафиксированных норм этики. Какие этические принципы там определены? Как они "стимулируют" участников следовать принятым нормам?
-
-
-### 2. Списки этических норм
-
-В то время как нормы профессионального поведения определяют необходимое _этичное поведение_ участников, они также принуждают к соблюдению [установленных ограничений](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md), особенно в крупных проектах. Вместо этого, многие эксперты в науке о данных [поддерживают использование списков](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md), которые **соединяют принципы и реальные практики** более определённым и действенным методом.
-
-Подобные списки конвертируют размытые вопросы в "да/нет" пункты, которые могут быть введены в действие и которые могут отслеживаться как часть стандартного процесса выпуска продукта.
-
-Примеры:
- * [Deon](https://deon.drivendata.org/) - список этических пунктов общего назначения, созданный по [промышленным рекомендациям](https://deon.drivendata.org/#checklist-citations) с поддержкой интерфейса командной строки для удобной интеграции.
- * [Список аудита приватности](https://cyber.harvard.edu/ecommerce/privacyaudit.html) содержит общие рекомендации для обработки информации с точки зрения правового и социального воздействия.
- * [Чеклист справедливого ИИ](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) создан разработчиками систем ИИ для поддержки интеграции проверок справедливости в цикл разработки продуктов с ИИ.
- * [22 вопросв о этике в данных и ИИ](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - более открытый список, ориентированный на первоначальное обнаружение этических проблем в контексте структуры, реализации и организации.
-
-### 3. Регулирование в области этики
-
-Этика определяет общие ценности и правильные поступки _на добровольной основе_. **Соглашение** в свою очередь принуждает _следовать закону_, если он определён, там, где он определён. **Руководство** покрывает широкий спектр способов, которыми организации пользуются для продвижения этических принципов и согласия с установленными законами.
-
-На сегодняшний день, руководство по этике принимает две формы внутри организации. Во-первых, оно определяет принципы **этичного ИИ** и устанавливает правила его внедрения для всех проектов организации, связанных с ИИ. Во-вторых, оно действует в соглашении с государственным **регулированием в области защиты данных** в регионах представительства организации.
-
-
-Примеры государственного регулирования в области защиты данных и конфиденциальности:
-
- * `1974 год`, [Закон США о конфиденциальности](https://www.justice.gov/opcl/privacy-act-1974), регулирующий сбор, использование и раскрытие персональной информации _на государственном уровне_.
- * `1996 год`, [Закон США о переносимости и подотчетности медицинского страхования (HIPAA)](https://www.cdc.gov/phlp/publications/topic/hipaa.html), защищающий персональные данные в сфере здравоохранения.
- * `1998 год`, [Закон США о защите конфиденциальности детей в Интернете (COPPA)](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule), защищающий конфиденциальность данных детей младше 13 лет.
- * `2018 год`, [Общие правила защиты данных (GDPR)](https://gdpr-info.eu/), обеспечивающий пользовательские права, защиту данных и конфиденциальность.
- * `2018`, [Закон Калифорнии о конфиденциальности потребителей (CCPA)](https://www.oag.ca.gov/privacy/ccpa), предоставляющий потребителям больше _прав_ в области своих (персональных) данных.
- * `2021`, китайский [Закон о защите личной информации](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/), принятый в недавном времени, применяющий одно из самых строгих в мире регулирований в области конфиденциальности данных в сети Интернет.
-
-> 🚨 Принятые Европейским Союзом Общие правила защиты данных (GDPR) остаются на сегодняшний день наиболее влиятельным регулированием в области приватности данных. Знали ли Вы, что они также определяют [8 прав пользователей](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr) в области защиты конфиденциальности и персональных данных граждан? Узнайте подробнее, что они из себя представляют и почему они имеют значение.
-
-### 4. Этичная культура
-
-Отметим, что остаётся неосязаемый промежуток между _соглашением_ (делать достаточно, чтобы оставаться "в рамках закона") и [системными проблемами](https://www.coursera.org/learn/data-science-ethics/home/week/4) (такими, как потеря гибкости, информационная несимметричность и несправедливое распределение), который может ускорить применение ИИ в качестве оружия.
-
-Решение этой проблемы кроется в [совместных подходах к определению этичных культур](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f), которые выстраивают эмоциональные связи и постоянные общие ценности _во всех организациях_ отрасли. Это требует более глубокой [формализации культуры в области этики данных](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) в организациях, позволяющей _любому_ [потянуть за ниточки](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (чтобы поднять вопрос этики на ранней стадии) и провести _оценку этичности_ (например, при найме на работу) основных критериев формирования команд в проектах с ИИ.
-
----
-## [Проверочный тест](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/3) 🎯
-## Дополнительные источники 
-
-Курсы и книги помогут Вам понять основные этические принципы и вызовы, а примеры из реальной практики помогут с прикладными вопросами этики в контексте реального мира. Вот некоторые ресурсы, с которых можно начать:
-
-* [Машинное обучение для начинающих (на англ.)](https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md) - курс о справедливости от компании Microsoft.
-* [Принципы ответственного ИИ](https://docs.microsoft.com/ru-ru/learn/modules/responsible-ai-principles/) - бесплатный курс от Microsoft Learn.
-* [Этика в науке о данных](https://resources.oreilly.com/examples/0636920203964) - электронная книга издательства O'Reilly (M. Loukides, H. Mason и др.)
-* [Этика науки о данных](https://www.coursera.org/learn/data-science-ethics#syllabus) - онлайн курс от Мичиганского университета.
-* [Этика без прикрас](https://ethicsunwrapped.utexas.edu/case-studies) - случаи из реальной практики от Техасского университета.
-
-# Домашнее задание
-
-[Опишите реальный случай из этики в науке о данных](assignment.ru.md)
diff --git a/1-Introduction/02-ethics/translations/assignment.hi.md b/1-Introduction/02-ethics/translations/assignment.hi.md
deleted file mode 100644
index 40ca448a..00000000
--- a/1-Introduction/02-ethics/translations/assignment.hi.md
+++ /dev/null
@@ -1,19 +0,0 @@
-## डेटा एथिक्स केस स्टडी लिखें
-
-## निर्देश
-
-आपने विभिन्न [डेटा नैतिकता चुनौतियों](README.hi.md#2-ethics-challenges) के बारे में सीखा है और [केस स्टडीज](README.hi.md#3-case-studies) के कुछ उदाहरण देखे हैं जो वास्तविक दुनिया में डेटा नैतिकता चुनौतियों को दर्शाते हैं। संदर्भ
-
-इस असाइनमेंट में, आप अपना खुद का केस स्टडी लिखेंगे, जो आपके अपने अनुभव से, या एक प्रासंगिक वास्तविक दुनिया के संदर्भ से डेटा नैतिकता चुनौती को दर्शाता है जिससे आप परिचित हैं। बस इन चरणों का पालन करें:
-
-1. `एक डेटा नैतिकता चुनौती चुनें'। प्रेरणा पाने के लिए [पाठ के उदाहरण](README.hi.md#2-ethics-challenges) देखें या [डीऑन चेकलिस्ट](https://deon.drivendata.org/examples/) जैसे ऑनलाइन उदाहरण देखें।
-
-2. `एक वास्तविक विश्व उदाहरण का वर्णन करें`। ऐसी स्थिति के बारे में सोचें जिसके बारे में आपने सुना है (शीर्षक, शोध अध्ययन इत्यादि) या अनुभवी (स्थानीय समुदाय), जहां यह विशिष्ट चुनौती हुई। चुनौती से संबंधित डेटा नैतिकता के सवालों के बारे में सोचें - और इस मुद्दे के कारण उत्पन्न होने वाले संभावित नुकसान या अनपेक्षित परिणामों पर चर्चा करें। बोनस अंक: संभावित समाधानों या प्रक्रियाओं के बारे में सोचें जिन्हें इस चुनौती के प्रतिकूल प्रभाव को खत्म करने या कम करने में मदद के लिए यहां लागू किया जा सकता है।
-
-3. `संबंधित संसाधन सूची प्रदान करें`। यह साबित करने के लिए कि यह एक वास्तविक दुनिया की घटना थी, एक या अधिक संसाधन (एक लेख, एक व्यक्तिगत ब्लॉग पोस्ट या छवि, ऑनलाइन शोध पत्र आदि के लिंक) साझा करें। बोनस अंक: संसाधनों को साझा करें जो घटना से संभावित नुकसान और परिणामों को भी प्रदर्शित करते हैं, या इसकी पुनरावृत्ति को रोकने के लिए उठाए गए सकारात्मक कदमों को उजागर करते हैं।
-
-## सरनामा
-
-अनुकरणीय | पर्याप्त | सुधार की जरूरत
---- | --- | -- |
-एक या अधिक डेटा नैतिकता चुनौतियों की पहचान की जाती है। <br/> <br/> केस स्टडी स्पष्ट रूप से एक वास्तविक दुनिया की घटना का वर्णन करती है जो उस चुनौती को दर्शाती है, और अवांछनीय परिणामों या इससे होने वाले नुकसान को उजागर करती है। <br/><br/> यह साबित करने के लिए कम से कम एक लिंक किया गया संसाधन है। | एक डेटा नैतिकता चुनौती की पहचान की गई है। <br/><br/> कम से कम एक प्रासंगिक नुकसान या परिणाम पर संक्षेप में चर्चा की गई है। <br/><br/> हालांकि चर्चा सीमित है या वास्तविक दुनिया की घटना के प्रमाण का अभाव है। | एक डेटा चुनौती की पहचान की जाती है। <br/><br/> हालांकि विवरण या संसाधन चुनौती को पर्याप्त रूप से प्रतिबिंबित नहीं करते हैं या यह साबित नहीं करते हैं कि यह वास्तविक दुनिया की घटना है। |
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-## 데이터 윤리 사례 연구 작성
-
-## 지침
-
-다양한 [데이터 윤리 과제](README?id=_2-ethics-challenges)에 대해 배웠고 실제 컨텍스트의 데이터 윤리 과제를 반영하는 [사례 연구](README?id=_3-case-studies)의 몇 가지 예를 보았습니다.
-
-이 과제에서는 자신의 경험이나 친숙한 관련 실제 상황에서 데이터 윤리 문제를 반영하는 사례 연구를 작성합니다. 다음 단계를 따르세요.
-
-1. `데이터 윤리 과제 선택`. [수업 예시](README?id=_2-ethics-challenges)를 보거나 [Deon 체크리스트](https://deon.drivedata.org/examples/)와 같은 온라인 예시를 탐색하여 영감을 얻으십시오.
-
-2. `실제 사례 설명`. 이러한 특정 문제가 발생한 상황(헤드라인, 연구 연구 등) 또는 경험했던(지역 커뮤니티) 상황에 대해 생각해 보십시오. 문제와 관련된 데이터 윤리 질문에 대해 생각하고 이 문제로 인해 발생하는 잠재적인 피해 또는 의도하지 않은 결과에 대해 논의합니다. 보너스 포인트: 이 문제의 부정적인 영향을 제거하거나 완화하기 위해 여기에 적용될 수 있는 잠재적 솔루션 또는 프로세스에 대해 생각하십시오.
-
-3. `관련 자료 목록 제공`. 하나 이상의 리소스(기사 링크, 개인 블로그 게시물 또는 이미지, 온라인 연구 논문 등)를 공유하여 이것이 실제 발생했음을 증명합니다. 보너스 포인트: 사고로 인한 잠재적 피해 및 결과를 보여주는 리소스를 공유하거나 재발을 방지하기 위해 취한 긍정적인 조치를 강조합니다.
-
-
-
-## 기준표
-
-모범 | 충분 | 개선 필요
---- | --- | -- |
-하나 이상의 데이터 윤리 문제가 식별됩니다. <br/> <br/> 사례 연구는 그 도전을 반영하는 실제 사건을 명확하게 설명하고 그로 인해 야기된 바람직하지 않은 결과 또는 피해를 강조합니다. <br/><br/> 이 문제가 발생했음을 증명하는 연결된 리소스가 하나 이상 있습니다. | 하나의 데이터 윤리 과제가 식별됩니다. <br/><br/> 적어도 하나의 관련 피해 또는 결과가 간략하게 논의됩니다. <br/><br/> 그러나 논의가 제한적이거나 실제 발생에 대한 증거가 부족합니다. | 데이터 챌린지가 식별됩니다. <br/><br/> 그러나 설명이나 리소스가 문제를 적절하게 반영하지 않거나 실제 상황임을 증명하지 못합니다. |
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-## Schrijf een case study over data-ethiek
-
-## Instructies
-
-Je hebt geleerd over verschillende [Data Ethics Challenges](../README.md#2-ethics-challenges) en enkele voorbeelden gezien van [Case Studies](../README.md#3-case-studies) die uitdagingen op het gebied van data-ethiek in de praktijk weerspiegelen.
-
-In deze opdracht schrijf je je eigen case study die een uitdaging op het gebied van data-ethiek weerspiegelt vanuit je eigen ervaring, of vanuit een relevante echte-wereld context die je kent. Volg simpelweg deze stappen:
-
-1. `Kies een uitdaging voor data-ethiek`. Bekijk de [lesvoorbeelden](../README.md#2-ethics-challenges) of verken online voorbeelden zoals [de Deon Checklist](https://deon.drivendata.org/examples/) om inspiratie op te doen.
-
-2. `Beschrijf een voorbeeld uit de echte wereld`. Denk aan een situatie waarvan je hebt gehoord (koppen, onderzoek enz.) of die je hebt meegemaakt (lokale gemeenschap), waarin deze specifieke uitdaging zich voordeed. Denk na over de vragen over gegevensethiek met betrekking tot de uitdaging - en bespreek de mogelijke schade of onbedoelde gevolgen die zich voordoen als gevolg van dit probleem. Bonuspunten: denk na over mogelijke oplossingen of processen die hier kunnen worden toegepast om de negatieve impact van deze uitdaging te elimineren of te verminderen.
-
-3. `Geef een lijst met gerelateerde bronnen`. Deel een of meer bronnen (links naar een artikel, een persoonlijke blogpost of afbeelding, online onderzoekspaper enz.) om te bewijzen dat dit een echt voorval was. Bonuspunten: deel bronnen die ook de mogelijke schade en gevolgen van het incident laten zien, of belicht positieve stappen die zijn genomen om herhaling te voorkomen.
-
-
-
-## Rubriek
-
-Uitstekend | Adquaat | Vereist verbetering
---- | --- | -- |
-Er zijn een of meer uitdagingen op het gebied van data-ethiek geïdentificeerd. <br/> <br/> De casestudy beschrijft duidelijk een incident uit de echte wereld dat die uitdaging weerspiegelt, en benadrukt ongewenste gevolgen of schade die het veroorzaakte. <br/><br/> Er is ten minste één gekoppelde bron om te bewijzen dat dit is gebeurd. | Er wordt één uitdaging op het gebied van gegevensethiek geïdentificeerd. <br/><br/> Minstens één relevante schade of gevolg wordt kort besproken. <br/><br/> De discussie is echter beperkt of er is geen bewijs van echt voorkomen. | Er wordt een data-uitdaging geïdentificeerd. <br/><br/> De beschrijving of bronnen geven echter niet voldoende de uitdaging weer of bewijzen niet voldoende dat het in de praktijk voorkomt. |
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-## Escreva um Caso de Estudo de Ética de Dados
-
-## Instruções
-
-Você aprendeu sobre vários [Desafios da Ética de Dados](README.pt-br.md#2-desafios-de-ética) e viu alguns exemplos de [Estudo de Casos](README.pt-br.md#3-estudo-de-casos) refletindo desafios da ética de dados em contextos do mundo real.
-
-Nessa tarefa você irá escrever o seu próprio estudo de caso refletindo um desafio da ética de dados de seu própria experiência, ou de um contexto relevante do mundo real que você está familiarizado. Apenas siga esses passos:
-
-1. `Escolha um Desafio da Ética de Dados`. Olhe [os exemplos da aula](README.pt-br.md#2-desafios-de-ética) ou explore exemplos onlines como [as Checklists da Deon](https://deon.drivendata.org/examples/) para se inspirar.
-
-2. `Descreva um Exemplo do Mundo Real`. Pense sobre a situação que você ouviu sobre (manchetes, pesquisas etc.) ou experienciou (comunidade local), onde esse desafio em específico aconteceu. Pense sobre as questões de ética de dados relacionadas ao desafio - e discuta os danos potenciais ou consequências não-ntencionais que são levantados por causa desse problema. Pontos bônus: pense sobre potenciais soluções ou precessos que podem ser aplicados aqui para ajuda a eliminar ou mitigar o impacto adverso desse desafio.
-
-3. `Forneça um Lista de Recursos Relacionados`. Compartilhe um ou mais recursos (links para artigos, posts ou imagens de blogs pessoais, artigos de pesquisa online etc.) para provar que isso acotnece no mundo real. Pontos bônus: compartilhe recursos que também mostrar potenciais danos e consequências de incidentes, ou destacam medidas positivas tomadas para prevenir sua recorrência.
-
-
-
-## Rubrica
-
-Exemplar | Adequado | Precisa melhorar
---- | --- | -- |
-Um ou mais desafios de ética de dados são identificados. <br/> <br/> O estudo de caso descreve claramente um incidente do mundo real refletindo aquele desafio, e destaca consequências não desejáveis ou danos que causou. <br/><br/> Existe pelo menos um recurso linkado para provar que isso aconteceu. |Um desafio da ética de dados é identificado. <br/><br/> Pelo menos um dano ou consequência relevante é discutido brevemete. <br/><br/> No entanto a discussão é limitada ou falta provas de uma ocorrência no mundo real. | Um desafio de dados é identificado. <br/><br/> No entanto a descrição ou recursos não refletem adequadamente o desafio ou provam que aconteceu no mundo real. |
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-## Опишите реальный случай из этики в науке о данных
-
-## Порядок выполнения
-
-Вы узнали о различных [проблемах в науке о данных](README.ru.md#2-проблемы-этики) и увидели некоторые [случаи из реальной практики](README.ru.md#3-случаи-из-реальной-практики), отражающие этические вызовы в контексте реального мира. 
-
-В данном домашнем задании Вам предстоит написать свой собственный пример, отражающий этический вызов, из Вашего личного опыта или окружения. Следуйте шагам:
-
-1. `Выберите этический вызов, относящийся к науке о данных`. Взгляните на  [примеры из лекции](README.ru.md#2-проблемы-этики) или ознакомьтесь с примерами из Интернета, например [список Deon](https://deon.drivendata.org/examples/).
-
-2. `Опишите случай из реальной практики`. Вспомните ситуацию, о которой Вы слышали (заголовки, исследования и т.п.) или в которой принимали участие (на местном уровне), когда именно этот этический вызов проявился. Обдумайте вопросы в рамках этики данных и обсудите ущерб или непреднамеренные последствия, которые имели место вследствие этой проблемы. Дополнительный вопрос: подумайте о потенциальных решениях или процессах, которые могли бы помочь смягчить или устранить неблагоприятное воздействие этого вызова.
-
-
-3. `Приведите список использованных источников`. Поделитесь одним или несколькими ресурсами (ссылки на статьи, пост в личном блоге или изображение, исследовательская онлайн статья и др.), чтобы подтвердить достоверность приведённого случая. Дополнительный вопрос: поделитесь ресурсами, которые описывают потенциальный ущерб и последствия от инцидента, или выделите положительные изменения для предотвращения его появления.
-
-
-## Оценка
-
-Отлично | Достаточно | Нуждается в улучшении
---- | --- | -- |
-Найден один или более этических вызовов. <br/> <br/> Приведённый пример полно описывает случай из реальной жизни, отражающий выбранный этический вызов и подчёркивающий нежелательные последствия или ущерб, которые он вызвал. <br/><br/> Приведён по крайней мере один источник в доказательство существования данной проблемы. | Приведёт один этический вызов. <br/><br/> Кратко описан по крайне мере один нанесённый ущерб или одно последствие. <br/><br/> Приведены неубедительные доказательства существования данной проблемы или не приведены вообще. | Вызов верно определён. <br/><br/> Приведённое описание или источники не отображают или не подтверждают существование выбранной проблемы. |
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diff --git a/1-Introduction/04-stats-and-probability/assignment.ipynb b/1-Introduction/04-stats-and-probability/assignment.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Introduction to Probability and Statistics\r\n",
-    "## Assignment\r\n",
-    "\r\n",
-    "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\r\n",
-    "df.head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
-       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
-       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
-       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
-       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
-       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>59</td>\n",
-       "      <td>2</td>\n",
-       "      <td>32.1</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>157</td>\n",
-       "      <td>93.2</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.8598</td>\n",
-       "      <td>87</td>\n",
-       "      <td>151</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>48</td>\n",
-       "      <td>1</td>\n",
-       "      <td>21.6</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>183</td>\n",
-       "      <td>103.2</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.8918</td>\n",
-       "      <td>69</td>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>72</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>156</td>\n",
-       "      <td>93.6</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.6728</td>\n",
-       "      <td>85</td>\n",
-       "      <td>141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>24</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25.3</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>198</td>\n",
-       "      <td>131.4</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.8903</td>\n",
-       "      <td>89</td>\n",
-       "      <td>206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>192</td>\n",
-       "      <td>125.4</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.2905</td>\n",
-       "      <td>80</td>\n",
-       "      <td>135</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 13
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\r\n",
-    "In this dataset, columns as the following:\r\n",
-    "* Age and sex are self-explanatory\r\n",
-    "* BMI is body mass index\r\n",
-    "* BP is average blood pressure\r\n",
-    "* S1 through S6 are different blood measurements\r\n",
-    "* Y is the qualitative measure of disease progression over one year\r\n",
-    "\r\n",
-    "Let's study this dataset using methods of probability and statistics.\r\n",
-    "\r\n",
-    "### Task 1: Compute mean values and variance for all values"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
-    "\r\n",
-    "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.8",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.8 64-bit (conda)"
-  },
-  "interpreter": {
-   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/1-Introduction/04-stats-and-probability/notebook.ipynb b/1-Introduction/04-stats-and-probability/notebook.ipynb
deleted file mode 100644
index 208eee50..00000000
--- a/1-Introduction/04-stats-and-probability/notebook.ipynb
+++ /dev/null
@@ -1,1122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction to Probability and Statistics\n",
-    "In this notebook, we will play around with some of the concepts we have previously discussed. Many concepts from probability and statistics are well-represented in major libraries for data processing in Python, such as `numpy` and `pandas`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import random\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Random Variables and Distributions\n",
-    "Let's start with drawing a sample of 30 values from a uniform distribution from 0 to 9. We will also compute mean and variance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sample: [4, 8, 5, 10, 5, 1, 1, 1, 7, 9, 7, 0, 2, 7, 3, 5, 9, 8, 3, 10, 2, 9, 2, 9, 9, 8, 1, 8, 7, 3]\n",
-      "Mean = 5.433333333333334\n",
-      "Variance = 10.178888888888887\n"
-     ]
-    }
-   ],
-   "source": [
-    "sample = [ random.randint(0,10) for _ in range(30) ]\n",
-    "print(f\"Sample: {sample}\")\n",
-    "print(f\"Mean = {np.mean(sample)}\")\n",
-    "print(f\"Variance = {np.var(sample)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To visually estimate how many different values are there in the sample, we can plot the **histogram**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(sample)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analyzing Real Data\n",
-    "\n",
-    "Mean and variance are very important when analyzing real-world data. Let's load the data about baseball players from [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Team</th>\n",
-       "      <th>Role</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Age</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Adam_Donachie</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>22.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Paul_Bako</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>215.0</td>\n",
-       "      <td>34.69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Ramon_Hernandez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>Catcher</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>30.78</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Kevin_Millar</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>72</td>\n",
-       "      <td>210.0</td>\n",
-       "      <td>35.43</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Chris_Gomez</td>\n",
-       "      <td>BAL</td>\n",
-       "      <td>First_Baseman</td>\n",
-       "      <td>73</td>\n",
-       "      <td>188.0</td>\n",
-       "      <td>35.71</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Brad_Thompson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>25.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1030</th>\n",
-       "      <td>Tyler_Johnson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>74</td>\n",
-       "      <td>180.0</td>\n",
-       "      <td>25.73</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1031</th>\n",
-       "      <td>Chris_Narveson</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>205.0</td>\n",
-       "      <td>25.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1032</th>\n",
-       "      <td>Randy_Keisler</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>75</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>31.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1033</th>\n",
-       "      <td>Josh_Kinney</td>\n",
-       "      <td>STL</td>\n",
-       "      <td>Relief_Pitcher</td>\n",
-       "      <td>73</td>\n",
-       "      <td>195.0</td>\n",
-       "      <td>27.92</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1034 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Name Team            Role  Height  Weight    Age\n",
-       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
-       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
-       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
-       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
-       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
-       "...               ...  ...             ...     ...     ...    ...\n",
-       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
-       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
-       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
-       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
-       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
-       "\n",
-       "[1034 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Height','Weight','Age'])\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> We are using a package called [**Pandas**](https://pandas.pydata.org/) here for data analysis. We will talk more about Pandas and working with data in Python later in this course.\n",
-    "\n",
-    "Let's compute average values for age, height and weight:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Age        28.736712\n",
-       "Height     73.697292\n",
-       "Weight    201.689255\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[['Age','Height','Weight']].mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's focus on height, and compute standard deviation and variance: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[74, 74, 72, 72, 73, 69, 69, 71, 76, 71, 73, 73, 74, 74, 69, 70, 72, 73, 75, 78]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(list(df['Height'])[:20])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean = 73.6972920696325\n",
-      "Variance = 5.316798081118074\n",
-      "Standard Deviation = 2.3058183105175645\n"
-     ]
-    }
-   ],
-   "source": [
-    "mean = df['Height'].mean()\n",
-    "var = df['Height'].var()\n",
-    "std = df['Height'].std()\n",
-    "print(f\"Mean = {mean}\\nVariance = {var}\\nStandard Deviation = {std}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to mean, it makes sense to look at the median value and quartiles. They can be visualized using a **box plot**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,2))\n",
-    "plt.boxplot(df['Height'], vert=False, showmeans=True)\n",
-    "plt.grid(color='gray', linestyle='dotted')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also make box plots of subsets of our dataset, for example, grouped by player role."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df.boxplot(column='Height', by='Role', figsize=(10,8))\n",
-    "plt.xticks(rotation='vertical')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> **Note**: This diagram suggests, that on average, the heights of first basemen are higher than heights of second basemen. Later we will learn how we can test this hypothesis more formally, and how to demonstrate that our data is statistically significant to show that.  \n",
-    "\n",
-    "Age, height and weight are all continuous random variables. What do you think their distribution is? A good way to find out is to plot the histogram of values: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df['Weight'].hist(bins=15, figsize=(10,6))\n",
-    "plt.suptitle('Weight distribution of MLB Players')\n",
-    "plt.xlabel('Weight')\n",
-    "plt.ylabel('Count')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Normal Distribution\n",
-    "\n",
-    "Let's create an artificial sample of weights that follows a normal distribution with the same mean and variance as our real data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([73.46072234, 70.40678311, 70.23689776, 73.81190675, 72.41091792,\n",
-       "       76.00127651, 71.91641414, 77.18162239, 76.7173353 , 73.93996587,\n",
-       "       74.2862748 , 76.88034696, 72.15184905, 74.43537605, 76.37723417,\n",
-       "       65.66976051, 74.3200533 , 77.3235274 , 72.8840488 , 77.50300255])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "generated = np.random.normal(mean, std, 1000)\n",
-    "generated[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(generated, bins=15)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(np.random.normal(0,1,50000), bins=300)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since most values in real life are normally distributed, we should not use a uniform random number generator to generate sample data. Here is what happens if we try to generate weights with a uniform distribution (generated by `np.random.rand`):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "wrong_sample = np.random.rand(1000)*2*std+mean-std\n",
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(wrong_sample)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Confidence Intervals\n",
-    "\n",
-    "Let's now calculate confidence intervals for the weights and heights of baseball players. We will use the code [from this stackoverflow discussion](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "p=0.85, mean = 201.73 ± 0.94\n",
-      "p=0.90, mean = 201.73 ± 1.08\n",
-      "p=0.95, mean = 201.73 ± 1.28\n"
-     ]
-    }
-   ],
-   "source": [
-    "import scipy.stats\n",
-    "\n",
-    "def mean_confidence_interval(data, confidence=0.95):\n",
-    "    a = 1.0 * np.array(data)\n",
-    "    n = len(a)\n",
-    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
-    "    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n",
-    "    return m, h\n",
-    "\n",
-    "for p in [0.85, 0.9, 0.95]:\n",
-    "    m, h = mean_confidence_interval(df['Weight'].fillna(method='pad'),p)\n",
-    "    print(f\"p={p:.2f}, mean = {m:.2f} ± {h:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hypothesis Testing\n",
-    "\n",
-    "Let's explore different roles in our baseball players dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Weight</th>\n",
-       "      <th>Count</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Role</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Catcher</th>\n",
-       "      <td>72.723684</td>\n",
-       "      <td>204.328947</td>\n",
-       "      <td>76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Designated_Hitter</th>\n",
-       "      <td>74.222222</td>\n",
-       "      <td>220.888889</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>First_Baseman</th>\n",
-       "      <td>74.000000</td>\n",
-       "      <td>213.109091</td>\n",
-       "      <td>55</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Outfielder</th>\n",
-       "      <td>73.010309</td>\n",
-       "      <td>199.113402</td>\n",
-       "      <td>194</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Relief_Pitcher</th>\n",
-       "      <td>74.374603</td>\n",
-       "      <td>203.517460</td>\n",
-       "      <td>315</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Second_Baseman</th>\n",
-       "      <td>71.362069</td>\n",
-       "      <td>184.344828</td>\n",
-       "      <td>58</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Shortstop</th>\n",
-       "      <td>71.903846</td>\n",
-       "      <td>182.923077</td>\n",
-       "      <td>52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Starting_Pitcher</th>\n",
-       "      <td>74.719457</td>\n",
-       "      <td>205.163636</td>\n",
-       "      <td>221</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Third_Baseman</th>\n",
-       "      <td>73.044444</td>\n",
-       "      <td>200.955556</td>\n",
-       "      <td>45</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                      Height      Weight  Count\n",
-       "Role                                           \n",
-       "Catcher            72.723684  204.328947     76\n",
-       "Designated_Hitter  74.222222  220.888889     18\n",
-       "First_Baseman      74.000000  213.109091     55\n",
-       "Outfielder         73.010309  199.113402    194\n",
-       "Relief_Pitcher     74.374603  203.517460    315\n",
-       "Second_Baseman     71.362069  184.344828     58\n",
-       "Shortstop          71.903846  182.923077     52\n",
-       "Starting_Pitcher   74.719457  205.163636    221\n",
-       "Third_Baseman      73.044444  200.955556     45"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby('Role').agg({ 'Height' : 'mean', 'Weight' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's test the hypothesis that First Basemen are taller than Second Basemen. The simplest way to do this is to test the confidence intervals:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Conf=0.85, 1st basemen height: 73.62..74.38, 2nd basemen height: 71.04..71.69\n",
-      "Conf=0.90, 1st basemen height: 73.56..74.44, 2nd basemen height: 70.99..71.73\n",
-      "Conf=0.95, 1st basemen height: 73.47..74.53, 2nd basemen height: 70.92..71.81\n"
-     ]
-    }
-   ],
-   "source": [
-    "for p in [0.85,0.9,0.95]:\n",
-    "    m1, h1 = mean_confidence_interval(df.loc[df['Role']=='First_Baseman',['Height']],p)\n",
-    "    m2, h2 = mean_confidence_interval(df.loc[df['Role']=='Second_Baseman',['Height']],p)\n",
-    "    print(f'Conf={p:.2f}, 1st basemen height: {m1-h1[0]:.2f}..{m1+h1[0]:.2f}, 2nd basemen height: {m2-h2[0]:.2f}..{m2+h2[0]:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the intervals do not overlap.\n",
-    "\n",
-    "A statistically more correct way to prove the hypothesis is to use a **Student t-test**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "T-value = 7.65\n",
-      "P-value: 9.137321189738925e-12\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.stats import ttest_ind\n",
-    "\n",
-    "tval, pval = ttest_ind(df.loc[df['Role']=='First_Baseman',['Height']], df.loc[df['Role']=='Second_Baseman',['Height']],equal_var=False)\n",
-    "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The two values returned by the `ttest_ind` function are:\n",
-    "* p-value can be considered as the probability of two distributions having the same mean. In our case, it is very low, meaning that there is strong evidence supporting that first basemen are taller.\n",
-    "* t-value is the intermediate value of normalized mean difference that is used in the t-test, and it is compared against a threshold value for a given confidence value."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulating a Normal Distribution with the Central Limit Theorem\n",
-    "\n",
-    "The pseudo-random generator in Python is designed to give us a uniform distribution. If we want to create a generator for normal distribution, we can use the central limit theorem. To get a normally distributed value we will just compute a mean of a uniform-generated sample."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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6HavqkUl+Mslbk2SM8Z0xxn/t+8Tra9JjMtu/JeihVbWR5AdztH9P/F7XctW3vdgsvRb2Ofcz6XFln3MvS6/lOu93BPI0O73d9r1+4FTV45O8LMlfXOiTVNVWkmcn+eTqRzw0pq7lNUl+O8l392m+w2LKOj4lyZ1J/mrx34ZvqaqH7eewa27ptRxjfDXJHyb5SpKzSb45xvjovk673nZdy4XnVdVnqupvquoZD/C2R8GUdfwe+5wk09fymtjnnDdlLdd2vyOQp9nL221fk+R3xhj37PgJqh6e7aNPrx9jfGu14x0qS69lVf1sknNjjJv2abbDZMpjciPJc5K8eYzx7CT/k+QoP99zymPy0dk+gvLkJD+c5GFV9Qv7MeQhsZe1/FSSJ40xnpnkT5N88AHc9qiYso7bn8A+57yl19I+536mPC7Xdr+z9BuFkGRvb7d9Isl7qipJjiV5cVXdPcb4YFU9ONs/qN45xrj+IAZeY0uvZZLnJvm5qnpxkockeWRVvWOMcRSDZMo6/nOSO8YY548qvS9r8oNqJlPW8sFJvjzGuDNJqur6JD+e5B37PfSa2nUte6yNMW6oqj+vqmN7ue0RsvQ6jjHuss+5lymPyefHPqeb+v29nvuduZ8EfZhP2f4HxpeyfZTo/BPTn/H/XP/t+f6LeCrJXye5Zu6vYx1OU9byPtuvyBF+wcTUdUzyD0mevjj/piR/MPfXdBjXMtv/aLs12889rmy/COW1c39N67yWSX4o33/zqsuz/fSUeqB/DxfzaeI62uesaC3vc50jvc9ZxVqu637HEeQJxgXebruqfm3x8Qs+7zjb/wJ9VZLPVtXNi21vHGPcsJ8zr6uJa8nCCtbxtUneWVWXZPsH3i/v68BrbMpajjE+WVXvy/Z/K96d5NM5ZG+zukp7XMuXJ/n1xRH4/03yirG9x9zxtrN8ITObso5V9ROxz/meiY9JmhWs5Vrud7zVNAAANF6kBwAAjUAGAIBGIAMAQCOQAQCgEcgAANAIZAAAaAQyAAA0/wceFVFs3MY9ywAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def normal_random(sample_size=100):\n",
-    "    sample = [random.uniform(0,1) for _ in range(sample_size) ]\n",
-    "    return sum(sample)/sample_size\n",
-    "\n",
-    "sample = [normal_random() for _ in range(100)]\n",
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(sample)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Correlation and Evil Baseball Corp\n",
-    "\n",
-    "Correlation allows us to find relations between data sequences. In our toy example, let's pretend there is an evil baseball corporation that pays its players according to their height - the taller the player is, the more money he/she gets. Suppose there is a base salary of $1000, and an additional bonus from $0 to $100, depending on height. We will take the real players from MLB, and compute their imaginary salaries:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(74, 1075.2469071629068), (74, 1075.2469071629068), (72, 1053.7477908306478), (72, 1053.7477908306478), (73, 1064.4973489967772), (69, 1021.4991163322591), (69, 1021.4991163322591), (71, 1042.9982326645181), (76, 1096.746023495166), (71, 1042.9982326645181)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "heights = df['Height']\n",
-    "salaries = 1000+(heights-heights.min())/(heights.max()-heights.mean())*100\n",
-    "print(list(zip(heights, salaries))[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's now compute covariance and correlation of those sequences. `np.cov` will give us a so-called **covariance matrix**, which is an extension of covariance to multiple variables. The element $M_{ij}$ of the covariance matrix $M$ is a correlation between input variables $X_i$ and $X_j$, and diagonal values $M_{ii}$ is the variance of $X_{i}$. Similarly, `np.corrcoef` will give us the **correlation matrix**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Covariance matrix:\n",
-      "[[  5.31679808  57.15323023]\n",
-      " [ 57.15323023 614.37197275]]\n",
-      "Covariance = 57.153230230544736\n",
-      "Correlation = 1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Covariance matrix:\\n{np.cov(heights, salaries)}\")\n",
-    "print(f\"Covariance = {np.cov(heights, salaries)[0,1]}\")\n",
-    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A correlation equal to 1 means that there is a strong **linear relation** between two variables. We can visually see the linear relation by plotting one value against the other:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(heights,salaries)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see what happens if the relation is not linear. Suppose that our corporation decided to hide the obvious linear dependency between heights and salaries, and introduced some non-linearity into the formula, such as `sin`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correlation = 0.9835304456670837\n"
-     ]
-    }
-   ],
-   "source": [
-    "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.mean()))*100\n",
-    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this case, the correlation is slightly smaller, but it is still quite high. Now, to make the relation even less obvious, we might want to add some extra randomness by adding some random variable to the salary. Let's see what happens:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correlation = 0.9363097848296155\n"
-     ]
-    }
-   ],
-   "source": [
-    "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.mean()))*100+np.random.random(size=len(heights))*20-10\n",
-    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(heights, salaries)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> Can you guess why the dots line up into vertical lines like this?\n",
-    "\n",
-    "We have observed the correlation between an artificially engineered concept like salary and the observed variable *height*. Let's also see if the two observed variables, such as height and weight, correlate too:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1., nan],\n",
-       "       [nan, nan]])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.corrcoef(df['Height'],df['Weight'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Unfortunately, we did not get any results - only some strange `nan` values. This is due to the fact that some of the values in our series are undefined, represented as `nan`, which causes the result of the operation to be undefined as well. By looking at the matrix we can see that `Weight` is the problematic column, because self-correlation between `Height` values has been computed.\n",
-    "\n",
-    "> This example shows the importance of **data preparation** and **cleaning**. Without proper data we cannot compute anything.\n",
-    "\n",
-    "Let's use `fillna` method to fill the missing values, and compute the correlation: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1.        , 0.52959196],\n",
-       "       [0.52959196, 1.        ]])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.corrcoef(df['Height'],df['Weight'].fillna(method='pad'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is indeed a correlation, but not such a strong one as in our artificial example. Indeed, if we look at the scatter plot of one value against the other, the relation would be much less obvious:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Height'],df['Weight'])\n",
-    "plt.xlabel('Height')\n",
-    "plt.ylabel('Weight')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "In this notebook we have learnt how to perform basic operations on data to compute statistical functions. We now know how to use a sound apparatus of math and statistics in order to prove some hypotheses, and how to compute confidence intervals for arbitrary variables given a data sample. "
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
deleted file mode 100644
index da16d87c..00000000
--- a/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ /dev/null
@@ -1,945 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Introduction to Probability and Statistics\r\n",
-    "## Assignment\r\n",
-    "\r\n",
-    "In this assignment, we will use the dataset of diabetes patients taken [from here](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "import pandas as pd\r\n",
-    "import numpy as np\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\r\n",
-    "df.head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
-       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
-       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
-       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
-       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
-       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>59</td>\n",
-       "      <td>2</td>\n",
-       "      <td>32.1</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>157</td>\n",
-       "      <td>93.2</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.8598</td>\n",
-       "      <td>87</td>\n",
-       "      <td>151</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>48</td>\n",
-       "      <td>1</td>\n",
-       "      <td>21.6</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>183</td>\n",
-       "      <td>103.2</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.8918</td>\n",
-       "      <td>69</td>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>72</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>156</td>\n",
-       "      <td>93.6</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.6728</td>\n",
-       "      <td>85</td>\n",
-       "      <td>141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>24</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25.3</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>198</td>\n",
-       "      <td>131.4</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.8903</td>\n",
-       "      <td>89</td>\n",
-       "      <td>206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>192</td>\n",
-       "      <td>125.4</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.2905</td>\n",
-       "      <td>80</td>\n",
-       "      <td>135</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 13
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\r\n",
-    "In this dataset, columns as the following:\r\n",
-    "* Age and sex are self-explanatory\r\n",
-    "* BMI is body mass index\r\n",
-    "* BP is average blood pressure\r\n",
-    "* S1 through S6 are different blood measurements\r\n",
-    "* Y is the qualitative measure of disease progression over one year\r\n",
-    "\r\n",
-    "Let's study this dataset using methods of probability and statistics.\r\n",
-    "\r\n",
-    "### Task 1: Compute mean values and variance for all values"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "df.describe()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "              AGE         SEX         BMI          BP          S1          S2  \\\n",
-       "count  442.000000  442.000000  442.000000  442.000000  442.000000  442.000000   \n",
-       "mean    48.518100    1.468326   26.375792   94.647014  189.140271  115.439140   \n",
-       "std     13.109028    0.499561    4.418122   13.831283   34.608052   30.413081   \n",
-       "min     19.000000    1.000000   18.000000   62.000000   97.000000   41.600000   \n",
-       "25%     38.250000    1.000000   23.200000   84.000000  164.250000   96.050000   \n",
-       "50%     50.000000    1.000000   25.700000   93.000000  186.000000  113.000000   \n",
-       "75%     59.000000    2.000000   29.275000  105.000000  209.750000  134.500000   \n",
-       "max     79.000000    2.000000   42.200000  133.000000  301.000000  242.400000   \n",
-       "\n",
-       "               S3          S4          S5          S6           Y  \n",
-       "count  442.000000  442.000000  442.000000  442.000000  442.000000  \n",
-       "mean    49.788462    4.070249    4.641411   91.260181  152.133484  \n",
-       "std     12.934202    1.290450    0.522391   11.496335   77.093005  \n",
-       "min     22.000000    2.000000    3.258100   58.000000   25.000000  \n",
-       "25%     40.250000    3.000000    4.276700   83.250000   87.000000  \n",
-       "50%     48.000000    4.000000    4.620050   91.000000  140.500000  \n",
-       "75%     57.750000    5.000000    4.997200   98.000000  211.500000  \n",
-       "max     99.000000    9.090000    6.107000  124.000000  346.000000  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>48.518100</td>\n",
-       "      <td>1.468326</td>\n",
-       "      <td>26.375792</td>\n",
-       "      <td>94.647014</td>\n",
-       "      <td>189.140271</td>\n",
-       "      <td>115.439140</td>\n",
-       "      <td>49.788462</td>\n",
-       "      <td>4.070249</td>\n",
-       "      <td>4.641411</td>\n",
-       "      <td>91.260181</td>\n",
-       "      <td>152.133484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>13.109028</td>\n",
-       "      <td>0.499561</td>\n",
-       "      <td>4.418122</td>\n",
-       "      <td>13.831283</td>\n",
-       "      <td>34.608052</td>\n",
-       "      <td>30.413081</td>\n",
-       "      <td>12.934202</td>\n",
-       "      <td>1.290450</td>\n",
-       "      <td>0.522391</td>\n",
-       "      <td>11.496335</td>\n",
-       "      <td>77.093005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>19.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>18.000000</td>\n",
-       "      <td>62.000000</td>\n",
-       "      <td>97.000000</td>\n",
-       "      <td>41.600000</td>\n",
-       "      <td>22.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.258100</td>\n",
-       "      <td>58.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>38.250000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>23.200000</td>\n",
-       "      <td>84.000000</td>\n",
-       "      <td>164.250000</td>\n",
-       "      <td>96.050000</td>\n",
-       "      <td>40.250000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.276700</td>\n",
-       "      <td>83.250000</td>\n",
-       "      <td>87.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>50.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>25.700000</td>\n",
-       "      <td>93.000000</td>\n",
-       "      <td>186.000000</td>\n",
-       "      <td>113.000000</td>\n",
-       "      <td>48.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.620050</td>\n",
-       "      <td>91.000000</td>\n",
-       "      <td>140.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>59.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>29.275000</td>\n",
-       "      <td>105.000000</td>\n",
-       "      <td>209.750000</td>\n",
-       "      <td>134.500000</td>\n",
-       "      <td>57.750000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>4.997200</td>\n",
-       "      <td>98.000000</td>\n",
-       "      <td>211.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>79.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>42.200000</td>\n",
-       "      <td>133.000000</td>\n",
-       "      <td>301.000000</td>\n",
-       "      <td>242.400000</td>\n",
-       "      <td>99.000000</td>\n",
-       "      <td>9.090000</td>\n",
-       "      <td>6.107000</td>\n",
-       "      <td>124.000000</td>\n",
-       "      <td>346.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 5
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "source": [
-    "# Another way\r\n",
-    "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "                AGE       SEX        BMI          BP           S1          S2  \\\n",
-       "Mean       48.51810  1.468326  26.375792   94.647014   189.140271  115.439140   \n",
-       "Variance  171.84661  0.249561  19.519798  191.304401  1197.717241  924.955494   \n",
-       "\n",
-       "                  S3        S4        S5          S6            Y  \n",
-       "Mean       49.788462  4.070249  4.641411   91.260181   152.133484  \n",
-       "Variance  167.293585  1.665261  0.272892  132.165712  5943.331348  "
-      ],
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Mean</th>\n",
-       "      <td>48.51810</td>\n",
-       "      <td>1.468326</td>\n",
-       "      <td>26.375792</td>\n",
-       "      <td>94.647014</td>\n",
-       "      <td>189.140271</td>\n",
-       "      <td>115.439140</td>\n",
-       "      <td>49.788462</td>\n",
-       "      <td>4.070249</td>\n",
-       "      <td>4.641411</td>\n",
-       "      <td>91.260181</td>\n",
-       "      <td>152.133484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Variance</th>\n",
-       "      <td>171.84661</td>\n",
-       "      <td>0.249561</td>\n",
-       "      <td>19.519798</td>\n",
-       "      <td>191.304401</td>\n",
-       "      <td>1197.717241</td>\n",
-       "      <td>924.955494</td>\n",
-       "      <td>167.293585</td>\n",
-       "      <td>1.665261</td>\n",
-       "      <td>0.272892</td>\n",
-       "      <td>132.165712</td>\n",
-       "      <td>5943.331348</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 8
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\r\n",
-    "df.mean()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "AGE     48.518100\n",
-       "SEX      1.468326\n",
-       "BMI     26.375792\n",
-       "BP      94.647014\n",
-       "S1     189.140271\n",
-       "S2     115.439140\n",
-       "S3      49.788462\n",
-       "S4       4.070249\n",
-       "S5       4.641411\n",
-       "S6      91.260181\n",
-       "Y      152.133484\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 9
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 2: Plot boxplots for BMI, BP and Y depending on gender"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "source": [
-    "for col in ['BMI','BP','Y']:\r\n",
-    "    df.boxplot(column=col,by='SEX')\r\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
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-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 3: What is the the distribution of Age, Sex, BMI and Y variables?"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\r\n",
-    "    df[col].hist()\r\n",
-    "    plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
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"
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-      "text/plain": [
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"
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Conclusions:\r\n",
-    "* Age - normal\r\n",
-    "* Sex - uniform\r\n",
-    "* BMI, Y - hard to tell"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 4: Test the correlation between different variables and disease progression (Y)\r\n",
-    "\r\n",
-    "> **Hint** Correlation matrix would give you the most useful information on which values are dependent."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "df.corr()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "          AGE       SEX       BMI        BP        S1        S2        S3  \\\n",
-       "AGE  1.000000  0.173737  0.185085  0.335428  0.260061  0.219243 -0.075181   \n",
-       "SEX  0.173737  1.000000  0.088161  0.241010  0.035277  0.142637 -0.379090   \n",
-       "BMI  0.185085  0.088161  1.000000  0.395411  0.249777  0.261170 -0.366811   \n",
-       "BP   0.335428  0.241010  0.395411  1.000000  0.242464  0.185548 -0.178762   \n",
-       "S1   0.260061  0.035277  0.249777  0.242464  1.000000  0.896663  0.051519   \n",
-       "S2   0.219243  0.142637  0.261170  0.185548  0.896663  1.000000 -0.196455   \n",
-       "S3  -0.075181 -0.379090 -0.366811 -0.178762  0.051519 -0.196455  1.000000   \n",
-       "S4   0.203841  0.332115  0.413807  0.257650  0.542207  0.659817 -0.738493   \n",
-       "S5   0.270774  0.149916  0.446157  0.393480  0.515503  0.318357 -0.398577   \n",
-       "S6   0.301731  0.208133  0.388680  0.390430  0.325717  0.290600 -0.273697   \n",
-       "Y    0.187889  0.043062  0.586450  0.441482  0.212022  0.174054 -0.394789   \n",
-       "\n",
-       "           S4        S5        S6         Y  \n",
-       "AGE  0.203841  0.270774  0.301731  0.187889  \n",
-       "SEX  0.332115  0.149916  0.208133  0.043062  \n",
-       "BMI  0.413807  0.446157  0.388680  0.586450  \n",
-       "BP   0.257650  0.393480  0.390430  0.441482  \n",
-       "S1   0.542207  0.515503  0.325717  0.212022  \n",
-       "S2   0.659817  0.318357  0.290600  0.174054  \n",
-       "S3  -0.738493 -0.398577 -0.273697 -0.394789  \n",
-       "S4   1.000000  0.617859  0.417212  0.430453  \n",
-       "S5   0.617859  1.000000  0.464669  0.565883  \n",
-       "S6   0.417212  0.464669  1.000000  0.382483  \n",
-       "Y    0.430453  0.565883  0.382483  1.000000  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>AGE</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.173737</td>\n",
-       "      <td>0.185085</td>\n",
-       "      <td>0.335428</td>\n",
-       "      <td>0.260061</td>\n",
-       "      <td>0.219243</td>\n",
-       "      <td>-0.075181</td>\n",
-       "      <td>0.203841</td>\n",
-       "      <td>0.270774</td>\n",
-       "      <td>0.301731</td>\n",
-       "      <td>0.187889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SEX</th>\n",
-       "      <td>0.173737</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.088161</td>\n",
-       "      <td>0.241010</td>\n",
-       "      <td>0.035277</td>\n",
-       "      <td>0.142637</td>\n",
-       "      <td>-0.379090</td>\n",
-       "      <td>0.332115</td>\n",
-       "      <td>0.149916</td>\n",
-       "      <td>0.208133</td>\n",
-       "      <td>0.043062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BMI</th>\n",
-       "      <td>0.185085</td>\n",
-       "      <td>0.088161</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.395411</td>\n",
-       "      <td>0.249777</td>\n",
-       "      <td>0.261170</td>\n",
-       "      <td>-0.366811</td>\n",
-       "      <td>0.413807</td>\n",
-       "      <td>0.446157</td>\n",
-       "      <td>0.388680</td>\n",
-       "      <td>0.586450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BP</th>\n",
-       "      <td>0.335428</td>\n",
-       "      <td>0.241010</td>\n",
-       "      <td>0.395411</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.242464</td>\n",
-       "      <td>0.185548</td>\n",
-       "      <td>-0.178762</td>\n",
-       "      <td>0.257650</td>\n",
-       "      <td>0.393480</td>\n",
-       "      <td>0.390430</td>\n",
-       "      <td>0.441482</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1</th>\n",
-       "      <td>0.260061</td>\n",
-       "      <td>0.035277</td>\n",
-       "      <td>0.249777</td>\n",
-       "      <td>0.242464</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.896663</td>\n",
-       "      <td>0.051519</td>\n",
-       "      <td>0.542207</td>\n",
-       "      <td>0.515503</td>\n",
-       "      <td>0.325717</td>\n",
-       "      <td>0.212022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S2</th>\n",
-       "      <td>0.219243</td>\n",
-       "      <td>0.142637</td>\n",
-       "      <td>0.261170</td>\n",
-       "      <td>0.185548</td>\n",
-       "      <td>0.896663</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.196455</td>\n",
-       "      <td>0.659817</td>\n",
-       "      <td>0.318357</td>\n",
-       "      <td>0.290600</td>\n",
-       "      <td>0.174054</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S3</th>\n",
-       "      <td>-0.075181</td>\n",
-       "      <td>-0.379090</td>\n",
-       "      <td>-0.366811</td>\n",
-       "      <td>-0.178762</td>\n",
-       "      <td>0.051519</td>\n",
-       "      <td>-0.196455</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.738493</td>\n",
-       "      <td>-0.398577</td>\n",
-       "      <td>-0.273697</td>\n",
-       "      <td>-0.394789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S4</th>\n",
-       "      <td>0.203841</td>\n",
-       "      <td>0.332115</td>\n",
-       "      <td>0.413807</td>\n",
-       "      <td>0.257650</td>\n",
-       "      <td>0.542207</td>\n",
-       "      <td>0.659817</td>\n",
-       "      <td>-0.738493</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.617859</td>\n",
-       "      <td>0.417212</td>\n",
-       "      <td>0.430453</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S5</th>\n",
-       "      <td>0.270774</td>\n",
-       "      <td>0.149916</td>\n",
-       "      <td>0.446157</td>\n",
-       "      <td>0.393480</td>\n",
-       "      <td>0.515503</td>\n",
-       "      <td>0.318357</td>\n",
-       "      <td>-0.398577</td>\n",
-       "      <td>0.617859</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.464669</td>\n",
-       "      <td>0.565883</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S6</th>\n",
-       "      <td>0.301731</td>\n",
-       "      <td>0.208133</td>\n",
-       "      <td>0.388680</td>\n",
-       "      <td>0.390430</td>\n",
-       "      <td>0.325717</td>\n",
-       "      <td>0.290600</td>\n",
-       "      <td>-0.273697</td>\n",
-       "      <td>0.417212</td>\n",
-       "      <td>0.464669</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.382483</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Y</th>\n",
-       "      <td>0.187889</td>\n",
-       "      <td>0.043062</td>\n",
-       "      <td>0.586450</td>\n",
-       "      <td>0.441482</td>\n",
-       "      <td>0.212022</td>\n",
-       "      <td>0.174054</td>\n",
-       "      <td>-0.394789</td>\n",
-       "      <td>0.430453</td>\n",
-       "      <td>0.565883</td>\n",
-       "      <td>0.382483</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 20
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Conclusion:\r\n",
-    "* The strongest correlation of Y is BMI and S5 (blood sugar). This sounds reasonable."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\r\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\r\n",
-    "    ax[i].scatter(df['Y'],df[n])\r\n",
-    "    ax[i].set_title(n)\r\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x500 with 3 Axes>"
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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Task 5: Test the hypothesis that the degree of diabetes progression is different between men and women"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "source": [
-    "from scipy.stats import ttest_ind\r\n",
-    "\r\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\r\n",
-    "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "T-value = -0.90\n",
-      "P-value: 0.3674449793083975\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Conclusion: p-value close to 0 (typically, below 0.05) would indicate high confidence in our hypothesis. In our case, there is no strong evidence that sex affects progression of diabetes."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.8",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.8 64-bit (conda)"
-  },
-  "interpreter": {
-   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file