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107 lines
2.0 KiB
107 lines
2.0 KiB
# Data Ethics
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> Summary Sketchnote from [Nitya Narasimhan](https://twitter.com/nitya) / [SketchTheDocs](https://twitter.com/sketchthedocs)
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<br/>
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## Pre-Lecture Quiz
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[Pre-lecture quiz]()
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## Introduction
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This lesson dives into a critical topic for the modern data scientist: _data ethics_.
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In this lesson we'll cover:
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1. _[Fundamentals](#1-fundamentals)_ - Principles & History
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2. _[Data Collection](#2-data-collection)_ - Ownership & Consent
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3. _[Data Privacy](#3-data-privacy)_ - Protection & Anonymity
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4. _[Algorithms & Fairness](#4-algorithms-and-fairness)_ - Unfairness, Harms & Bias
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5. _[Tools & Frameworks](5-tools-and-frameworks)_ - Codes, Checklists & Frameworks
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6. _[Summary](6-summary)_ - Related Work
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<br/>
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## 1. Fundamentals
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| Topics|
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|--|
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| 1.1 What is Ethics and why do we care?|
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| 1.2 History and challenges |
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| 1.3 Concepts in Ethics|
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| 1.4 Ethical Principles and Responsible AI|
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<br/>
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## 2. Data Collection
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| Topics|
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| 2.1 Data Ownership & Intellectual Property |
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| 2.2 Ethics & Human Consent |
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| 2.3 Data Quality & Representation |
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| 2.4 The 5Cs Framework |
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<br/>
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## 3. Data Privacy
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| Topics|
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| 3.1 Data Privacy & Degrees of Privacy |
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| 3.2 Data Anonymity & De-Identification |
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| 3.3 Challenges & Frameworks |
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| 3.4 Case Studies |
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<br/>
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## 4. Algorithms and Fairness
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| Topics|
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| 4.1 Fairness, Unfairness & Harms |
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| 4.2 Data Validity & Misrepresentation |
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| 4.3 Algorithm Bias & Mitigation |
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| 4.4 Case Studies |
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<br/>
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## 5. Tools and Frameworks
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| Topics|
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| 5.1 Data Ethics & Culture |
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| 5.2 Codes of Conduct & Checklists |
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| 5.3 Industry Frameworks (Google, IBM, Microsoft, Facebook) |
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| 5.4 Government Frameworks (UK, US, India) |
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<br/>
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## 6. Summary
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| Topics|
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| 6.1 Understanding Ethics (History) |
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| 6.2 Applying Ethics (Principles) |
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| 6.3 Evolving Ethics (Research) |
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| 6.4 Further Reading (References) |
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<br/>
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## 🚀 Challenge
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## Post-Lecture Quiz
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[Post-lecture quiz]()
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## Review & Self Study
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## Assignment
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[Assignment Title](assignment.md)
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