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Output attribution, content watermarking, and AI detection mechanisms.
Also in Non-Model
AI identifiers can be used to indicate that an AI is involved in a process or an interaction [40]. For AI systems that interact directly with users, a visible output may be used, e.g., a displayed text message saying “I am an AI language model”, accompanied with the appropriate warnings and caveats relevant to the user, [40]. Whereas, for AI systems that interact with other systems or applications, other forms of watermark or unique identifiers can be used. In either situation, agent cards can serve as an identifier, where further details about the underlying AI system, the specific instance of the AI agent, and other information relevant to the development of the agent, can be included.
Reasoning
Technical mechanism enabling attribution and detection of AI-generated content through watermarking or provenance tracking.
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.
Deploy
Releasing the AI system into a production environment
Deployer
Entity that integrates and deploys the AI system for end users
Manage
Prioritising, responding to, and mitigating AI risks
