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Red teaming, capability evaluations, adversarial testing, and performance verification.
Also in Risk & Assurance
Contamination detection refers to techniques for assessing whether and to what extent a given model has benchmark data in its training dataset [161, 170]. This can involve a set of technical and regulatory interventions to prevent or identify a model trained on contaminated data.
For example, with web-crawled data, contamination detection can involve comparing the data’s web sources against a dynamic, publicly available blocklist of websites known to generate new benchmarks. Additional measures may include excluding data with improper metadata from the training dataset and conducting overlap analyses between the training data and all known standard benchmark datasets.
Reasoning
Testing mechanism that evaluates whether training data appears in model outputs, detecting memorization risks.
Benchmarking
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.
Collect and Process Data
Gathering, curating, labelling, and preprocessing training data
Developer
Entity that creates, trains, or modifies the AI system
Measure
Quantifying, testing, and monitoring identified AI risks
