Introducing the AI Governance and Regulatory Archive (AGORA): An Analytic Infrastructure for Navigating the Emerging AI Governance Landscape - Arnold, Zachary; Schiff, Daniel S.; Schiff, Kaylyn Jackson; Love, Brian; Melot, Jennifer; Singh, Neha; Jenkins, Lindsay; Lin, Ashley; Pilz, Konstantin; Enweareazu, Ogadinma; et al. (Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2024)
AI-related laws, standards, and norms are emerging rapidly. However, a lack of shared descriptive concepts and monitoring infrastructure undermine efforts to track, understand, and improve AI governance. We introduce AGORA (the AI Governance and Regulatory Archive), a rigorously compiled and enriched dataset of AI-focused laws and policies encompassing diverse jurisdictions, institutions, and contexts related to AI. AGORA is oriented around an original taxonomy describing risks, potential harms, governance strategies, incentives for compliance, and application domains addressed in AI regulatory documents. At launch, AGORA included data on over 330 instruments, with new entries being added continuously. We describe the manual and automated processes through which these data are systematically compiled, screened, annotated, and validated, enabling deep, efficient, and reliable analysis of the emerging AI governance landscape. The dataset, supporting information, and analyses are available through a public web interface (https://agora.eto.tech) and bulk dataset.