19-716 Responsible AI and AI Governance: Identifying and Mitigating Risks in the Design
6–12 units, Carnegie Mellon University
As AI and machine learning systems become integral to products and services across industries, it is critical to identify and mitigate the risks associated with their design, deployment, and operation. This course examines the evolving landscape of AI governance, exploring both technical and organizational strategies for developing trustworthy and responsible AI systems. In 2025, the course expands to cover responsible development and governance of Agentic AI—systems capable of autonomous reasoning, planning, and collaboration. Students explore governance strategies across the AI lifecycle, including model alignment (RLHF, RLAIF), fairness, differential privacy, explainability, interpretability, and AI red teaming. The course integrates evolving policy and regulatory frameworks such as the EU AI Act, NIST's AI Risk Management Framework, ISO/IEC 42001, and OECD guidelines. Case studies examine responsible AI practices in foundation models, generative systems, and agent-based ecosystems. The course combines technical, policy, and management perspectives to equip students with the tools and frameworks needed to assess and mitigate AI-related risks.
Prerequisites: none listed
Offered: Spring 2027, Spring 2026
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