Principles of Data Science Ethics

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Overview

Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics. News stories appear regularly about credit algorithms that discriminate against women, medical algorithms that discriminate against African Americans, hiring algorithms that base decisions on gender, and more. In most cases, those who developed and deployed these algorithms and data processes had no such intentions, and were unaware of the harmful impact of their work....

Syllabus

This course is arranged in 4 modules. We estimate that you will need 5 hours per week. The course is self-paced, so you have the flexibility to complete the modules in your own time. Week 1 Landscape of Harm

Videos:
- AI and Big Brother
- Unintended harm
- Types of harm
- Best Practices - CRISP-DM
- A bit of ancient history (verified users only)
Knowledge Checks
Reading / Discussion Prompt 1
Exercise 1 & 2 (for verified users only)

Week 2 Legal Issues

Videos:
- Legal Issues EU
- Existing laws
Knowledge Checks
Reading / Discussion Prompt 2
Exercise 3 (for verified users only)

Week 3 Transparency

Videos:
- Model interpretability
- Global interpretability methods
Knowledge Checks
Reading
Exercise 4 & 5 (for verified users only)

Week 4 Principles and Frameworks

Videos:
- Introduction to Principles of Responsible Data Science (RDS)
- From Principles to Practice
- RDS Framework
- Return to CRISP-DM
Knowledge Checks
Exercise 6 (for verified users only)