AI Policy Forum Symposium
ABOUT THIS EVENT
The MIT AI Policy Forum is convening its second AI Policy Forum Symposium on May 19, 2022 from 9:00 am to 4:00 pm EDT. This event will comprise four panels held virtually, and each will bring together members of the public sector, private sector, and academia to discuss critical questions in AI policy.
Thursday, May 19, 2022
9:00 am—4:00 pm (EDT)
Design questions for AI laws
AI-related legislation has been rapidly emerging around the world. The United States enacted the National Artificial Intelligence Initiative Act; Europe is moving forward with its own EU AI Act; and China too has advanced several new regulations. Each of these developments represents a different approach to legislating AI, but what makes a good AI law? How do we get there?
- Eva Kaili (European Parliament, Vice President)
- Ro Khanna (US House of Representatives, Congressman)
- Bitange Ndemo (University of Nairobi, Professor)
- Jonathan Zittrain (Harvard Law & Berkman Klein Center, Faculty Director)
- Dan Huttenlocher (MIT Schwarzman College of Computing, Dean) moderator
Auditing and monitoring AI systems at scale
There is a growing consensus that auditing and monitoring of AI systems needs to be at the core of any AI regulation. Yet, it is still not clear how to effectively implement such capabilities — and to do so at the scale that broad real-world deployment necessitates. This is complicated further by the increasing outsourcing of AI and the emergent use of large “core” models. How should we navigate this complicated technical and policy landscape?
- Diane Lye (Capital One, Executive Vice President, CIO Card and Small Business Technology)
- Aleksander Madry (MIT, Professor)
- Elham Tabassi (NIST, ITL Chief of Staff)
- Renaud Vedel (France, Ministerial Coordinator for AI)
- Luis Videgaray (MIT, Senior Lecturer) moderator
Data sharing and privacy in clinical AI
Advances in computing, in general, and AI, in particular, offer great opportunities for the clinical healthcare sector. In particular, applying machine learning to large datasets stands to substantially improve medical diagnosis and decision making. However, making this promise a reality will require that practitioners be able to pool and share data coming from different sources with relative ease. What are the hurdles that we still face and the relevant tradeoffs that we need to navigate in this context?
- Douglas Fridsma (Datavant, Head of Government Partnerships)
- Anna Goldenberg (University of Toronto, Associate Professor)
- Elliott Green (Dandelion Health AI, CEO)
- Jonathan Sellors (UK Biobank, Legal Counsel)
- David Sontag (MIT, Professor)
- Marcy Wilder (Hogan Lovells, Partner)
- Marzyeh Ghassemi (MIT, Professor) moderator
Realizing the benefits of autonomous vehicles
Bringing the vision of autonomous vehicles to its full fruition requires tackling not only (tremendous) technical challenges and opportunities but also a host of policy issues. What policies can ensure that autonomous mobility meets the needs of underserved populations and integrates with public transportation systems and other mobility options? How can standards and regulation ensure the optimal trade-off between mobility system performance (e.g., congestion, emissions) and overall system safety? How can the advancement of innovation in this space continue apace while developing sufficient societal trust in the process?
- Emilio Frazzoli (ETH Zurich, Professor)
- Prashanthi Raman (Cruise, Vice President of Global Government Affairs)
- Mark Rosekind (Zoox, Chief Safety Innovation Officer)
- Dawn Woodard (LinkedIn, Distinguished Scientist)
- Jinhua Zhao (MIT, Professor) moderator
- Aleksander Madry (MIT, Professor)