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Shared evaluation datasets, testing frameworks, and measurement tools for AI systems.
Also in Shared Infrastructure
A model is situationally aware if it internally represents that it is a machine learning model and if it can accurately infer or act on model-relevant facts - e.g., if it is currently in training, testing, evaluation or deployment, or the desired outcome of an evaluation. Some benchmarks exist for situational awareness of AI models, which test whether the AI models can classify stereotypical inputs from training, testing, evaluation and deployment as such, and whether the AI model can use this information correctly to take actions in the world [25, 111].
Reasoning
Shared evaluation dataset enabling standardized situational awareness assessment across organizations.
Situational awareness
Model development
2.4 Engineering & DevelopmentModel development > Data-related
1.1 ModelModel evaluations
2.2.2 Testing & EvaluationModel evaluations > General evaluations
2.2.2 Testing & EvaluationModel evaluations > Benchmarking
3.2.1 Benchmarks & EvaluationModel evaluations > Red teaming
2.2.2 Testing & EvaluationRisk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
Gipiškis, Rokas; San Joaquin, Ayrton; Chin, Ze Shen; Regenfuß, Adrian; Gil, Ariel; Holtman, Koen (2024)
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 fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023-2025, which were extracted into a living database of 831 distinct AI risk mitigations.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Developer
Entity that creates, trains, or modifies the AI system
Measure
Quantifying, testing, and monitoring identified AI risks
