Data Ethics: Initial Lesson Plan

Organized as 6 sections with 4 units per section.
Provides high-level coverage of concepts, challenges & frameworks.
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Nitya Narasimhan 3 years ago
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# Data Ethics
> Summary Sketchnote from [Nitya Narasimhan](https://twitter.com/nitya) / [SketchTheDocs](https://twitter.com/sketchthedocs)
<br/>
## 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]()_ - Principles & History
2. _[Data Collection](#2-data-collection)_ - Ownership & Consent
3. _[Data Privacy](#2-data-privacy)_ - Protection & Anonymity
4. _[Fairness](#4-fairness)_ - Algorithm Bias &
5. _[Tools](5-tools)_ - 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 |
<br/>
## 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 |
<br/>
## 4. Algorithms Fairness
| Topics|
|--|
| 4.1 Fairness, Unfairness & Harms |
| 4.2 Data Validity & Misrepresentation |
| 4.3 Algorithm Bias & Mitigation |
| 4.4 Case Studies |
<br/>
## 5. Tools
| 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) |
<br/>
## 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
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[Post-lecture quiz]()
## Review & Self Study

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