This report presents an analytic approach to help U.S. policymakers deconstruct artificial intelligence governance proposals by identifying their underlying assumptions, which are the foundational elements that facilitate the success of a proposal. By applying the approach to five U.S.-based AI governance proposals from industry, academia, and civil society, as well as state and federal government, this report demonstrates how identifying assumptions can help policymakers make informed, flexible decisions about AI under uncertainty.

As artificial intelligence systems are deployed and affect more aspects of daily life, effective risk mitigation becomes imperative to prevent harm. This report analyzes AI incidents to improve our understanding of how risks from AI materialize in practice. By identifying six mechanisms of harm, it sheds light on the different pathways to harm, and on the variety of mitigation strategies needed to address them.

This report analyzes over 250 scientific publications that use open language models in ways that require access to model weights and derives a taxonomy of use cases that open weights enable. The authors identified a diverse range of seven open-weight use cases that allow researchers to investigate a wider scope of questions, explore more avenues of experimentation, and implement a larger set of techniques.

In the second installation of our blog series analyzing 147 AI-related laws enacted by Congress between January 2020 and March 2025 from AGORA, we explore the governance strategies, risk-related concepts, and harms addressed in the legislation. In the first blog, we showed that the majority of these AI-related legislative documents were drawn from National Defense Authorization Acts and apply to national security contexts.

Organizations looking to adopt artificial intelligence (AI) systems face the challenge of deciphering a myriad of voluntary standards and best practices—requiring time, resources, and expertise that many cannot afford. To address this problem, this report distills over 7,000 recommended practices from 52 reports into a single harmonized framework. Integrating new AI guidance with existing safety and security practices, this work provides a road map for organizations navigating the complex landscape of AI guidance.

This report examines lessons from the North Carolina Life Sciences Apprenticeship Consortium for pharmaceutical and biomanufacturing workforce development, and analyzes how apprenticeship programs help address workforce shortages in emerging tech fields. It offers a practical framework with important considerations for designing and launching programs, and serves as a resource for employers, regional leaders, and policymakers seeking to build a more resilient and technically skilled workforce.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. This three-part series introduces CSET’s patent clusters, which connect related patents through citations and text similarity.

In this two-part analysis, we use data from the Emerging Technology Observatory's AGORA to explore AI-related legislation that was enacted by Congress between January 2020 and March 2025. This first blog explores the origin and application domains of the AI-related legislation we reviewed. The second blog examines the governance strategies, risk-related concepts, and harms covered by this legislation.

This roundtable report explores how practitioners, researchers, educators, and government officials view work-based learning as a tool for strengthening the cybersecurity workforce. Participants engaged in an enriching discussion that ultimately provided insight and context into what makes work-based learning unique, effective, and valuable for the cyber workforce.

System-to-model innovation is an emerging innovation pathway in artificial intelligence that has driven progress in several prominent areas over the last decade. System-level innovations advance with the diffusion of AI and expand the base of contributors to leading-edge progress in the field. Countries that can identify and harness system-level innovations faster and more comprehensively will gain crucial economic and military advantages over competitors. This paper analyzes the benefits of system-to-model innovation and suggests a three-part framework to navigate the policy implications: protect, diffuse, and anticipate.