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Data Ethics
Summary Sketchnote from Nitya Narasimhan / SketchTheDocs
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:
- Fundamentals - Principles & History
- Data Collection - Ownership & Consent
- Data Privacy - Protection & Anonymity
- Algorithms & Fairness - Unfairness, Harms & Bias
- Tools & Frameworks - Codes, Checklists & Frameworks
- Summary - Related Work
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 |
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 |
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 |
4. Algorithms and Fairness
| Topics |
| 4.1 Fairness, Unfairness & Harms |
| 4.2 Data Validity & Misrepresentation |
| 4.3 Algorithm Bias & Mitigation |
| 4.4 Case Studies |
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) |
6. Summary
| Topics |
| 6.1 Understanding Ethics (History) |
| 6.2 Applying Ethics (Principles) |
| 6.3 Evolving Ethics (Research) |
| 6.4 Further Reading (References) |
🚀 Challenge
Post-Lecture Quiz
Post-lecture quiz
Review & Self Study
Assignment
Assignment Title
Nitya Narasimhan
9747ff9a37