Week 12: AI Ethics, Bias, Fairness, and Governance
Dates: Mar 29-Apr 2 · Reading: Handout 10: AI Ethics, Bias, and Fairness
Learning Objectives
- Define fairness in machine learning and different fairness definitions
- Identify sources of bias in training data, model design, and deployment
- Explain major AI governance frameworks (GDPR, AI Act, U.S. Executive Orders)
- Discuss transparency and explainability in AI systems
Monday Session
What is fairness in ML? Bias in training data, model design, and deployment. Legal and ethical frameworks: GDPR, AI Act, U.S. Executive Orders. Transparency and explainability.
Wednesday Session
Different fairness definitions and trade-offs. Regulatory requirements. How organizations build responsible AI: governance boards, audits, and documentation.
Lab
Lab 10: Bias Detection in Models. Analyze a pre-trained model for bias across demographic groups, compute fairness metrics, and discuss mitigation.
Quiz / This Week
Quiz 9. Fairness definitions; bias sources; governance frameworks; regulatory requirements.