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Governance frameworks, formal policies, and strategic alignment mechanisms.
Also in Oversight & Accountability
It’s crucial that decisions about training or deploying AI systems at companies are appropriately authorised, and comply with other requirements around safety.
Change management security measures might look like: - Requiring multi-person signoff to approve large training runs, deployments or configuration changes - Splitting up duties between different people to avoid one party gaining too much control - Recording and reviewing [audit logs](https://adamjones.me/blog/ai-regulator-toolbox/#audit-logging) of changes - Conducting security impact assessments when making security-critical changes
Reasoning
Multi-person authorization, duty separation, and audit logging control access to critical deployment decisions.
Compute 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 (multiple stages)
Applies across multiple lifecycle stages
Other (multiple actors)
Applies across multiple actor types
Govern
Policies, processes, and accountability structures for AI risk management
