IAP 2020: AI Technology, Policy, and Society: Priorities, Opportunities, and Trade-Offs
STS.S91/16.S498 – January 13-17, 10 a.m. – 3 p.m. (1-hr lunch break). E51-385. Course readings.
Description: This seminar will explore the current alignment of AI Technology, Public Policy, and Society with an eye toward identifying a wide range of governance approaches that can guide the ongoing integration of AI-driven systems in our economy and society. We will assess the relative maturity of technologies that are essential to AI’s positive contribution to society, consider governance tools available to support positive developments of AI, and protect citizens around the world from harmful effects of these new systems.
By considering legal, policy, ethical, and social-theoretical perspectives on current and proposed uses of AI, we will learn about how society’s values and priorities are and can be reflected in the future, and what trade-offs we should be prepared to address. Each day we will hear presentations from scholars who focus on engineering, social-scientific, and public policy topics. At the close of the course, students will have an opportunity to develop recommendations for how the MIT community should engage with the technology, public policy, industrial, and civil society actors in the future of AI Policy.
|R. David Edelman (IPRI)|
Gary Gensler (Sloan)
|David Kaiser (STS/Physics)|
John Leonard (MechE)
Aleksandr Madry (EECS)
Taylor Reynolds (IPRI)
|Julie Shah (AeroAstro)|
Luis Videgaray (Sloan/IPRI)
Daniel Weitzner (IPRI/CSAIL)
Monday - 1/13
|Course Goals: Kaiser, Shah, Weitzner|
From Global Principles to Local Practice: AI Policy Around the World: Edelman (Slides)
Tools, Levers and Trade-offs in the Search for Ethical AI Policy: Videgaray (Slides)
Tuesday - 1/14
|Guest Lecture: AI & Transportation Policy: Safety, Standards, and Public Trust: Nat Beuse (Head of Safety, Uber Advanced Technologies Group (ATG), Former Associate Administrator, NHTSA, DOT)|
Challenges and opportunities in automated driving: John Leonard
AI in Finance – The Opportunities, Policy Considerations and Trade-offs: Gensler (Slides)
Wednesday - 1/15
|Consumer protection & Privacy - Weitzner (Slides)|
Interpretability and Explanation: Shah and students ( Slides)
Robustness of ML Systems: Madry (Slides)
Synthesis: Key technical factors influencing policy values, strategies and trade-offs
Thursday - 1/16
|Perspectives from the History of the Internet: Weitzner|
Principles and Methods for Measuring AI Maturity and Progress: Reynolds (Slides)
Each class session will include a lecture with discussion period. At the end of each day, students and faculty work together to identify concrete contributions to the global AI Policy dialogue as well as future research topics. Students are welcome to register for credit but not required to do so. A short reading list will be provided.