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Runtime behavior observation, anomaly detection, and activity logging.
Also in Non-Model
A dashboard [41] displays all the relevant information about the model’s internal state and the model’s physical properties to the user. It is used to ensure that the user is informed about factors that influence the behavior of the model and to ensure that the user maintains control over the model. Allowing only the user to access the dashboard can aid in information asymmetry between the user and model, thus supporting the user oversight over the model.
Examples of the model’s internal state include its representations of the world, representation of users, and the strategies it is currently pursuing; while examples of the model’s physical properties include the model’s compute and energy consumption, physical storage occupied, and physical networks it is connected to.
Reasoning
Dashboard compiles documented evidence of model properties for assurance purposes.
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.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Developer
Entity that creates, trains, or modifies the AI system
Measure
Quantifying, testing, and monitoring identified AI risks
