Course description
Principles of Data Science Ethics
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.
Who should attend?
Prerequisites:
Predictive Analytics: Basic Modeling Techniques
Training content
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)
Legal Issues
- Videos:
- Legal Issues EU
- Existing laws
- Knowledge Checks
- Reading / Discussion Prompt 2
- Exercise 3 (for verified users only)
Transparency
- Videos:
- Model interpretability
- Global interpretability methods
- Knowledge Checks
- Reading
- Exercise 4 5 (for verified users only)
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)
Course delivery details
This course is offered through Statistics.com, a partner institute of EdX.
4-5 hours per week
Costs
- Verified Track -$198.97
- Audit Track - Free
Certification / Credits
What you'll learn
After completing this course you should be able to:
- Identify and anticipate the types of unintended harm that can arise from AI models
- Explain why interpretability is key to avoiding harm
- Distinguish between intrinsically interpretable models and black box models
- Evaluate tradeoffs between model performance and interpretability
- Establish a Responsible Data Science framework for your projects
About edX
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