The Societal Impact of Foundation Models: Advancing Evidence-based AI Policy
It addresses the societal impact of foundation models for policymakers and researchers, but it is incremental as it builds on existing work in AI governance without introducing new technical methods.
This dissertation tackles the challenge of understanding and governing foundation models in AI by developing a conceptual framework, empirical insights, and policy recommendations to improve societal outcomes, though it does not provide concrete numerical results.
Artificial intelligence is humanity's most promising technology because of the remarkable capabilities offered by foundation models. Yet, the same technology brings confusion and consternation: foundation models are poorly understood and they may precipitate a wide array of harms. This dissertation explains how technology and society coevolve in the age of AI, organized around three themes. First, the conceptual framing: the capabilities, risks, and the supply chain that grounds foundation models in the broader economy. Second, the empirical insights that enrich the conceptual foundations: transparency created via evaluations at the model level and indexes at the organization level. Finally, the transition from understanding to action: superior understanding of the societal impact of foundation models advances evidence-based AI policy. View together, this dissertation makes inroads into achieving better societal outcomes in the age of AI by building the scientific foundations and research-policy interface required for better AI governance.