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Implementation standards, guidelines, and documented best practices for AI development.
Also in Shared Infrastructure
Reasoning
Policy implementation is too vague without description to distinguish whether it addresses technical systems, organizational structures, or ecosystem-level mechanisms.
Regulator design
Design effective structures and strategies for regulatory bodies.
3.1.2 Regulatory BodiesLegislation and regulation development
Turn policy ideas into specific rules that can be legally enforced, and usually some powers to enforce them.
3.1.1 Legislation & PolicyStandards development
Turn ideas for making AI safer into specific implementation requirements.
3.2.2 Technical StandardsCompute goverance
Regulate companies in the highly concentrated AI chip supply chain, given AI chips are key inputs to developing frontier AI models.
3.1.1 Legislation & PolicyData input controls
Filter data used to train AI models, e.g. don’t train your model with instructions to launch cyberattacks.
1.1.1 Training DataLicensing
Require organisations or specific training runs to be licensed by a regulatory body, similar to licensing regimes in other high-risk industries.
3.1.4 Compliance RequirementsOn-chip governance mechanisms
Make alterations to AI hardware (primarily AI chips), that enable verifying or controlling the usage of this hardware.
1.2.4 Security InfrastructureSafety cases
Develop structured arguments demonstrating that an AI system is unlikely to cause catastrophic harm, to inform decisions about training and deployment.
2.2.4 Assurance DocumentationEvaluations (aka “evals”)
Give AI systems standardised tests to assess their capabilities, which can inform the risks they might pose.
2.2.2 Testing & EvaluationThe AI regulator’s toolbox: A list of concrete AI governance practices
Jones, Adam (2024)
This article explains concrete AI governance practices people are exploring as of August 2024. Prior summaries have mapped out high-level areas of work, but rarely dive into concrete practice details. This summary explores specific practices addressing risks from advanced AI systems. Practices are grouped into categories based on where in the AI lifecycle they best fit. The primary goal of this article is to help newcomers contribute to the field of AI governance by providing a comprehensive overview of available practices.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
Primary
6.5 Governance failure