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Modifications to training data composition, quality, and filtering that affect what the model learns.
Also in Model
Developers of GPAIS and creators of benchmarks can take actions to prevent AI models from being trained on contaminated or leaked data, or mitigate such data contamination and leakage.
For example, developers of AI models can try to find and remove contaminated or leaked data from the training corpus, and creators of benchmarks can help them by adding globally unique “canary strings” to the documents containing their benchmarks, which makes them easier to find [197]. More involved interventions by benchmark-creators include restricting access to benchmarks over an API, or continually updating benchmarks to focus on recent data.
Reasoning
Filters training data to prevent contamination and leakage before model learning occurs.
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
Manage
Prioritising, responding to, and mitigating AI risks
