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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.
This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects and an audit process to follow in reviewing them. Case studies with Python code are provided.
After a course session ends, it will be archived.
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.
This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects, and an audit process to follow in reviewing them. Case studies along with Python code are provided.
After completing this course you should be able to:
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)
