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Laws, legal frameworks, and binding policy instruments governing AI development and use.
Also in Legal & Regulatory
Legal protections for people reporting AI risks, or non-compliance with AI regulations.
Whistleblower protections might include: - Employment protection: Safeguarding whistleblowers from being fired, demoted, or otherwise penalised for reporting issues. - Legal immunity: Make non-disclosure, non-disparagement or other agreements unenforceable if they do not clearly exclude reporting genuine concerns to responsible bodies. - Contract protection: Preventing auditors or third parties from being fired or otherwise penalised for reporting genuine issues to regulators. - Access protection: Preventing researchers or those critical of AI systems from being banned from public AI tools, or otherwise not having access they would otherwise have. - Anonymity: Allowing whistleblowers to report issues anonymously, with legal penalties for revealing their identity. - Financial support: Providing compensation or support for whistleblowers who face financial hardship as a result of their actions. This could also incentivise reporting genuine problems, similar to [the SEC’s whistleblower awards programme](https://www.sec.gov/enforcement-litigation/whistleblower-program).
Reasoning
Legal framework establishing whistleblower protections and enforcement mechanisms for AI risk reporting, requiring state authority.
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 (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
