Interactive Discussion on A Path for Science‑ and Evidence‑based AI Policy

Abstract: As ML is becoming ubiquitous and creating new and impressive artifacts, regulatory agencies around the world are grappling with the unique properties of this new category of technology. In order to properly address these challenges, we argue for an approach centered on the dual pillars of rights and transparency to ensure that the technology is subject to the appropriate democratic governance. We outline some of the recent developments and proposals made by policymakers in this direction, how they connect to AI, and provide both organizational and technical tools to support well-informed regulation aligned with technology development now and in the future.

Bio (Dawn Song): Professor Song is the Director of Berkeley RDI; she works in AI, AI safety & security, and decentralization. Her work has won numerous awards, including MacArthur Fellowship, Guggenheim Fellowship, more than 10 Test-of-Time Awards and Best Paper Awards at top Machine Learning and Computer Security conferences, She is ranked the most cited scholar in computer security. She is also a serial entrepreneur, having founded multiple successful startups, named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

Bio (Rishi Bommasani): Rishi Bommasani is the Society Lead at the Stanford Center for Research on Foundation Models. He researches the societal impact of foundation models and advances evidence-based AI policy. Rishi's work has been featured in The Atlantic, MIT Technology Review, Nature, The New York Times, Quanta, Reuters, Science, The Wall Street Journal and The Washington Post. He serves as one of the chairs of EU AI Act Code of Practice and authors of the International Scientific Report on the Safety of Advanced AI.