This module explores major approaches to AI governance and the different ways institutions attempt to shape the development and use of AI systems. Students will map the AI governance ecosystem, identify key actors and policy tools, and compare approaches such as risk-based and rights-based policy frameworks. The module also examines how governance frameworks distribute responsibility across actors, and how non-legislative mechanisms like technical standards can influence AI practice.

This module examines how AI challenges existing ideas about data ownership, privacy, and authenticity. Students will explore how generative AI complicates copyright law, how AI intensifies longstanding privacy problems through data collection, inference, surveillance, and automated decision-making, and why provenance and consent are difficult to establish in AI training data. The module also considers the rise of deepfakes and synthetic media as challenges for dignity, democracy, and security. Throughout, students will consider what legal, technical, and governance responses might help address data harms.

This module examines how AI governance responds to the concrete impacts of AI systems on individuals and communities. Students will explore how automated decision-making can produce bias and discrimination, and what makes such decisions legitimate or contestable when they affect people's lives. The module then turns to AI's consequences for labor and the environment, and the governance tools that might address them. Finally, students will consider questions of responsibility and liability: when an AI system causes harm, who is accountable, and how responsibility can be distributed and misplaced. Throughout, students will weigh legal, organizational, and technical responses to these impacts.

This module turns from analyzing AI governance to designing it. Students will examine how organizations translate high-level AI principles into everyday practice, and why responsible AI efforts can fall short when they meet the realities of institutions, incentives, and resources. Building on the rest of the course, the module introduces a layered model for thinking about governance and walks through how to design a governance strategy, including framing the problem, choosing interventions, and stress testing approaches. Throughout, students will consider not only what governance should achieve in principle but how it actually works in practice, as well as the roles they might play in shaping it.