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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
+
+<br/>
+
+## 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|
+
+<br/>
+
+## 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) |
+
+
+<br/>
+
 ## 🚀 Challenge
 
 
@@ -11,6 +97,7 @@
 
 [Post-lecture quiz]()
 
+
 ## Review & Self Study