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Response plans, escalation procedures, and incident management for operational emergencies.
Also in Operations & Security
Enabling reports from the public, such as users and researchers.
Reasoning
Responsible disclosure programme enables structured identification and characterization of potential harms from external reporters.
Reporting channels
Provide ways for any external person or organisation to report an issue with an AI system.
2.3.4 Incident ResponseBug bounties
Provide (usually monetary) rewards to people making genuine reports.
2.3.4 Incident ResponseShared reports
Share data about reports, usually publicly and after the issue has been fixed.
3.3.1 Industry CoordinationCompute 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.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
Other
