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Cross-organization coordination mechanisms, information sharing, and collaborative monitoring.
Also in Voluntary & Cooperative
In some cases, AI system deployers are better positioned to perform certain risk management measures on the AI model in a provided AI system, relative to upstream model providers. For example, they understand their use case better and are more easily able to predict foreseeable misuse or failure modes. These evaluations can inform upstream model providers, or inform supplementary mitigations by the deployer.
Reasoning
Downstream provider evaluates models for deployment-specific failure modes through testing and assessment activities.
Monitoring
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.
Verify and Validate
Testing, evaluating, auditing, and red-teaming the AI system
Deployer
Entity that integrates and deploys the AI system for end users
Measure
Quantifying, testing, and monitoring identified AI risks
Other
