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Output attribution, content watermarking, and AI detection mechanisms.
Also in Non-Model
Output produced or whose production is aided by AI can contain metadata to record its origin and the transformations it has undergone. Metadata is evidence that subsequent versions or its derivatives come from this original version. The metadata can include the original AI model source, along with ownership, and its subsequent edits [169].
For example, an image produced by an AI model can contain metadata showing the date of creation and the AI model that produced it. Subsequent versions can reference this information and, if they are intermediary versions, can include descriptions of any editing that has taken place.
Reasoning
Metadata attached to AI outputs enables attribution and provenance tracking of generated content.
Post-deployment practices
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
Manage
Prioritising, responding to, and mitigating AI risks
