This course addresses the dual imperatives of security and responsibility in modern AI development. Students explore how to build AI systems that are resilient against attacks while respecting privacy, promoting fairness, and maintaining transparency. The curriculum integrates technical security measures with ethical frameworks, covering topics such as adversarial machine learning, differential privacy, fairness metrics, explainable AI, adversarial robustness, bias detection and mitigation, secure model training and deployment, and reliability assurance.

This course assumes students have some working knowledge of writing AI code and developing machine learning applications. A background in security (an intro course) and AI (an intro course) is preferred but not required.

Faculty and instructors who have taught this course in the past:

Hanan Hibshi