activity
20232026
most citedBenchmark Early and Red Team Often: A Framework for Assessing and Managing Dual-Use Hazards of AI Foundation Models

3 citations · 7 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CY2026

Open Problems in AI Risk Modeling: Insights from a Workshop on the Technical Foundations of AI Risk Modeling

Krystal Jackson, Deepika Raman, Jakub Kryś +8

We investigate the design of robust risk models to assess societal risks posed by advanced AI systems, an emerging area in AI governance. Many regulatory proposals increasingly req…

cs.CY2026

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…

cs.CR2026

Toward Risk Thresholds for AI-Enabled Cyber Threats: Enhancing Decision-Making Under Uncertainty with Bayesian Networks

Krystal Jackson, Deepika Raman, Jessica Newman +3

Artificial intelligence (AI) is increasingly being used to augment and automate cyber operations, altering the scale, speed, and accessibility of malicious activity. These shifts r…

cs.AI2025★ 1 cited

AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models

Anthony M. Barrett, Jessica Newman, Brandie Nonnecke +5

Increasingly multi-purpose AI models, such as cutting-edge large language models or other 'general-purpose AI' (GPAI) models, 'foundation models,' generative AI models, and 'fronti…

cs.CY2025★ 2 cited

Intolerable Risk Threshold Recommendations for Artificial Intelligence

Deepika Raman, Nada Madkour, Evan R. Murphy +2

Frontier AI models -- highly capable foundation models at the cutting edge of AI development -- may pose severe risks to public safety, human rights, economic stability, and societ…

cs.CR2024★ 3 cited

Benchmark Early and Red Team Often: A Framework for Assessing and Managing Dual-Use Hazards of AI Foundation Models

Anthony M. Barrett, Krystal Jackson, Evan R. Murphy +2

A concern about cutting-edge or "frontier" AI foundation models is that an adversary may use the models for preparing chemical, biological, radiological, nuclear, (CBRN), cyber, or…

cs.CR2023★ 1 cited

A Systematic Review of Machine Learning Enabled Phishing

Krystal A. Jackson

Developments in artificial intelligence (AI) are likely to affect social engineering and change cyber defense operations. The broad and sweeping nature of AI impact means that many…