About
This course explores how causal methods enhance AI-model trustworthiness. Students apply causal models to represent cause-effect relationships governing human understanding, improving generalization to novel data and yielding fairer, more interpretable results. Topics include AI robustness, privacy, safety, and accountability; tradeoffs among assumptions; associational, causal, and counterfactual conclusions; and trustworthiness architectures.