# Data Ethics

> Summary Sketchnote from [Nitya Narasimhan](https://twitter.com/nitya) / [SketchTheDocs](https://twitter.com/sketchthedocs)

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## Pre-Lecture Quiz

[Pre-lecture quiz]()

## Introduction

This lesson dives into a critical topic for the modern data scientist: _data ethics_.

In this lesson we'll cover:
 1. _[Fundamentals](#1-fundamentals)_ - Principles & History
 2. _[Data Collection](#2-data-collection)_ - Ownership & Consent
 3. _[Data Privacy](#3-data-privacy)_ - Protection & Anonymity
 4. _[Algorithms & Fairness](#4-algorithms-and-fairness)_ - Unfairness, Harms & Bias 
 5. _[Tools & Frameworks](5-tools-and-frameworks)_ - Codes, Checklists & Frameworks
 6. _[Summary](6-summary)_ - Related Work

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## 1. Fundamentals

| Topics|
|--|
| 1.1 What is Ethics and why do we care?|
| 1.2 History and challenges |
| 1.3 Concepts in Ethics|
| 1.4 Ethical Principles and Responsible AI|

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## 2. Data Collection

| Topics|
|--|
| 2.1 Data Ownership & Intellectual Property |
| 2.2 Ethics & Human Consent |
| 2.3 Data Quality & Representation |
| 2.4 The 5Cs Framework |


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## 3. Data Privacy

| Topics|
|--|
| 3.1 Data Privacy & Degrees of Privacy |
| 3.2 Data Anonymity & De-Identification |
| 3.3 Challenges & Frameworks |
| 3.4 Case Studies |

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## 4. Algorithms and Fairness

| Topics|
|--|
| 4.1 Fairness, Unfairness & Harms |
| 4.2 Data Validity & Misrepresentation |
| 4.3 Algorithm Bias & Mitigation |
| 4.4 Case Studies |

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## 5. Tools and Frameworks

| Topics|
|--|
| 5.1 Data Ethics & Culture |
| 5.2 Codes of Conduct & Checklists |
| 5.3 Industry Frameworks (Google, IBM, Microsoft, Facebook) |
| 5.4 Government Frameworks (UK, US, India) |

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## 6. Summary

| Topics|
|--|
| 6.1 Understanding Ethics (History) |
| 6.2 Applying Ethics (Principles) |
| 6.3 Evolving Ethics (Research) |
| 6.4 Further Reading (References) |


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## 🚀 Challenge


## Post-Lecture Quiz

[Post-lecture quiz]()


## Review & Self Study


## Assignment

[Assignment Title](assignment.md)