The Societal Impact of Foundation Models: Advancing Evidence-based AI Policy

TL;DR

This paper explores the societal impacts of foundation models, emphasizing the importance of understanding their capabilities, risks, and supply chains to inform evidence-based AI policy and improve governance.

Contribution

It provides a comprehensive framework linking technological capabilities, societal risks, empirical evaluations, and policy actions for foundation models.

Findings

Empirical evaluations improve transparency of foundation models.

Indexes at organizational levels inform societal impact assessments.

Understanding the supply chain aids in better AI governance.

Abstract

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.