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Cannot be confidently classified due to insufficient information, excessive vagueness, or ambiguity.
Impacts of AI
More energy-efficient models or techniques
Deploying more energy-efficient models can reduce their environmental impact. Different model architectural choices result in varying environmental costs, and identifying and adopting more energy-efficient options can result in significant environmental savings, especially when implemented at scale. Consideration should be given to both training energy usage, and deployment (“inference”) usage for the expected model lifecycle
1.1 ModelDisclosure of energy consumption by AI systems to authorities
Disclosures can direct more necessary attention and scrutiny to projects that consume significant energy. Disclosure involves releasing a summary of key details of the energy consumption of the AI system by all users, including the compute resources used, the amount of power consumed, the measures to reduce excess energy consumption that were in place, and energy sources
3.1.4 Compliance RequirementsUsing low carbon intensity energy grids
Moving model training to energy grids with low carbon intensity can reduce the negative environmental impact [30]. The efficiency of energy grids can vary greatly depending on location. Models can be trained in different locations, as latency is not an issue
1.2.9 OtherModel 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.
Unable to classify
Could not be classified to a specific lifecycle stage
Deployer
Entity that integrates and deploys the AI system for end users
Unable to classify
Could not be classified to a specific AIRM function
Primary
6.6 Environmental harm