Learning objectives
- • Compare global and local explanations such as permutation importance and SHAP
- • Discuss fairness metrics and why they can conflict
- • Frame responsible AI controls as product and operational requirements
Traditional ML
Interpretability, Fairness, and Responsible ML
Cover model explanations, fairness metrics, bias audits, and governance trade-offs for sensitive ML products.
Recommended on day 3890 minutesAdvanced
Interview prompts
- • How would you explain a rejected loan prediction to a regulator?
- • Why can two fairness metrics be impossible to satisfy at once?
Prerequisites