44 citations · 54 across the 10 of their papers we have counts for
11 papers
Mapping U.S. Federal AI Governance Against Sector Vulnerability
Ho Ting Hung, Angelica Chowdhury, James Teague +5
Artificial intelligence (AI) poses different levels of risk across sectors, but are these differences reflected in U.S. federal AI governance? To help answer this question, we asse…
Assessing Company Contributions to Societal Resilience: Extending the Societal Capacity Assessment Framework to Agentic AI
Catherine Simons, Alexander K. Saeri, Peter Slattery +1
Companies that deploy AI agents and make them available to others are creating the sociotechnical circumstances under which this technology integrates into existing social and econ…
FLARE-AI: Flaw Reporting for AI
Shayne Longpre, Elaine Zhu, Carson Ezell +15
Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify…
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
Open Problems in Frontier AI Risk Management
Marta Ziosi, Miro Plueckebaum, Stephen Casper +26
Frontier AI both amplifies existing risks and introduces qualitatively novel challenges. Not only is there a notable lack of stable scientific consensus resulting from the rapid pa…
AI Incident Monitoring through a Public Health Lens
Sophia Abraham, Taiye Chen, Cyril Chhun +5
Artificial intelligence systems are now deployed at scale across sectors, accompanied by a growing number of real-world incidents ranging from misinformation and cybercrime to auto…
A pragmatic classification framework for AI incident monitoring
Isaak Mengesha, Branwen Owen, Charlie Collins +4
Incident monitoring can drive safety improvements in high-reliability industries and population-scale technologies, but remains underdeveloped in AI governance. Public databases ca…
Mapping AI Risk Mitigations: Evidence Scan and Preliminary AI Risk Mitigation Taxonomy
Alexander K. Saeri, Sophia Lloyd George, Jess Graham +4
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is frag…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
A Taxonomy of Systemic Risks from General-Purpose AI
Risto Uuk, Carlos Ignacio Gutierrez, Daniel Guppy +5
Through a systematic review of academic literature, we propose a taxonomy of systemic risks associated with artificial intelligence (AI), in particular general-purpose AI. Followin…
The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence
Peter Slattery, Alexander K. Saeri, Emily A. C. Grundy +7
Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet researchers, policymakers, and technology c…