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Runtime behavior observation, anomaly detection, and activity logging.
Also in Non-Model
Incorporating the estimation of rare atypical input samples or classes might improve a model’s reliability, both with respect to its predictions and confidence calibration. Model predictions for rare inputs and classes may have a tendency of being overconfident and have worse accuracy scores [232]. For LLMs, the negative log-likelihood can be used as an atypicality measure. For discriminative models, Gaussian Mixture Models can be employed to estimate conditional and marginal distributions, which are then used in atypicality measurement.
Reasoning
Modifies training data composition by incorporating atypical samples to improve model reliability.
Fine-tuning-related
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.
Build and Use Model
Training, fine-tuning, and integrating the AI model
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
