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Shared evaluation datasets, testing frameworks, and measurement tools for AI systems.
Also in Shared Infrastructure
Stage: Detection; Stakeholder: Third Party Researchers; Additional information: AI developers and researchers should refine detection by developing standardised benchmarks and improving their reliability and validity. Developers should enhance detection of control-undermining capabilities.
Reasoning
Develops shared evaluation benchmarks and techniques for ecosystem-wide adoption by researchers.
Monitor critical capability levels
2.2.2 Testing & EvaluationIdentify early warning signs and emergent capabilities
2.2.1 Risk AssessmentEstablish standardised benchmarks and reporting
3.2.1 Benchmarks & EvaluationImplement compute monitoring and anomaly detection
1.2.3 Monitoring & DetectionEnhance hardware and supply chain oversight
2.3.3 Monitoring & LoggingLead efforts to establish shared criteria for AI LOC
3.2.2 Technical StandardsStrengthening Emergency Preparedness and Response for AI Loss of Control Incidents
Somani, Elika; Friedman, Anjay; Wu, Henry; Lu, Marianne; Byrd, Christopher; van Soest, Henri; Zakaria, Sana (2025)
As artificial intelligence (AI) systems become increasingly embedded in essential infrastructure and services, the risks associated with unintended failures rise. Developing comprehensive emergency response protocols could help mitigate these significant risks. This report focuses on understanding and addressing AI loss of control (LOC) scenarios where human oversight fails to adequately constrain an autonomous, general-purpose AI.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Other
Actor type not captured by the standard categories
Measure
Quantifying, testing, and monitoring identified AI risks
