Ethical and Responsible AI

Principles of Ethical Artificial Intelligence across technical and societal dimensions. Combines technical AI and machine learning implementations and ethical analysis. Students will learn to build, audit, and mitigate ethical risks in AI systems using tools like fairness libraries, explainability frameworks, and privacy-preserving techniques. Emphasizes coding, algorithmic critique, and real-world cases.
Topics include: foundations of AI ethics, fairness, interpretability, explainability,  accountability, privacy, robustness, alignment, safety, and societal benefit. 
Assessments include coding projects, bias auditing assignments, and ethical analysis papers.