Research on governing advanced AI and its risks. www.governance.ai
Recommends nine changes to how GPAI developers assess, mitigate and report biological risks under the EU AI Act, covering trusted access, post-deployment monitoring, safeguard evaluation and standardised uplift reporting.
Maps five technical safeguards against extreme CBRN and cyber misuse by frontier AI, covering model, deployment and governance levels, with current practice, evidence, limitations and research priorities.
The report assesses whether AI could enable a $100 billion cyberattack on the US power grid, finding the binding constraint is coordination capacity rather than technical bottlenecks, and suggests $10 billion attacks may be better capability thresholds for mitigations.
Builds a risk model for frontier AI enabling data-damaging worms via elite exploits, estimates economic damages from expert and forecaster surveys, and argues AI companies should evaluate for this capability.
Analyses how future AI could raise terrorist bomb-plot risk through added technical skill and reduced detection by law enforcement, arguing AI risk assessments should cover evasion of detection as well as dual-use knowledge.
Proposes a framework for converting frontier AI companies' dual-use biological capability evaluations into estimates of bioterrorism risk, using case studies, expert elicitation, and forecasting.
Expert and forecaster survey estimates biological catastrophe risk from LLMs, finds models already match top virologists on troubleshooting, and shows safeguards and DNA screening lower forecast risk.
Proposes a structured safety case template using the CAE framework to argue that frontier AI models lack unacceptable offensive cyber capabilities, linking risk models, proxy tasks and evaluations.
An opinion piece by Anderljung and Scharre arguing that society must prepare for powerful AI, using a case where an AI system was induced to plan synthesis of chemical weapons to motivate frontier AI governance.