Examining Policy Frameworks using the AI Governance and Regulatory Archive (AGORA)

• In Event: AI Governance, Regulation, and Public Institutions

Abstract

The rapid integration of artificial intelligence (AI) into public and private sectors has outpaced federal regulatory frameworks in the U.S., leaving states to act as de facto laboratories for AI governance. States have increasingly introduced and passed AI regulation primarily focusing on data privacy, accountability, and ethical deployment. However, the lack of federal coordination risks creating a fragmented regulatory landscape, complicating compliance for multistate actors and undermining equitable protections. State laws represent public demands for consumer protection from AI harms and potential challenges for AI companies in terms of navigating patchwork compliance burdens. Laws relating to practices like safety standards, data safeguards, and other issues could have national implications, particularly if they result in court cases that set precedent. However, in the current absence of such cases, the implications of these laws for U.S. competitiveness and national security is difficult to assess. AGORA is a valuable tool for compiling, summarizing, and categorizing AI governance documents, and current efforts have centered around state AI laws. 
AGORA (AI Governance and Regulatory Archive) is a living collection of AI-related laws, regulations, and standards. The dataset includes 1,025 AI policy documents (and counting) from the United States and other countries, with document summaries, full text, and tags for application areas, risk factors, and governance strategies. AGORA launched as an exploration and analysis tool for AI-relevant laws, regulations, standards, and other governance documents. Curated insights are provided in blog/substack posts sent to thousands of subscribers and AGORA documents and metadata are also provided as an open dataset, including document text, metadata, summaries, and thematic tags to enable deep analysis of the global AI governance landscape.
Our work addresses two core questions:  What distinctions exist in regulatory approaches across states, and how do these reflect divergent priorities (e.g., innovation vs. consumer protection or interests tied to state-specific industries), scopes (e.g., sectoral coverage), regulatory targets (e.g., data, compute, model weights) and capacity building efforts (e.g., strengthening AI-enabling talent and building evaluation infrastructure)?  Which state-level initiatives offer scalable best practices or approaches for balancing AI’s economic potential with reliable safeguards against bias, privacy violations, and transparency gaps?  This research aligns with broader goals of identifying policy models that reconcile AI advancement with governance best practices in democratic societies. By analyzing different legislative approaches and challenges, the effort will inform cohesive state, multistate, or federal AI governance strategies.

Authors

• Veronica Jade Kinoshita, Georgetown University
  Presenting Author

• Shruti Agarwal, Georgetown University
  Non-Presenting Co-Author