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Structured analysis to identify, characterize, and prioritize potential harms and risks.
Also in Risk & Assurance
Reasoning
Mitigation name lacks description; insufficient detail to identify focal activity or classification category.
Risk analysis
Understand and assess risks from AI systems
2.2.1 Risk AssessmentIncident monitoring
Investigate when things go wrong with AI systems, and learn from this.
2.3.3 Monitoring & LoggingOpen-source intelligence monitoring
Use public information to monitor compliance with AI standards, regulations or treaties.
3.1.4 Compliance RequirementsSemi-structured interviews
Conduct regular interviews with employees from frontier AI companies to gain insights into AI progress, risks, and internal practices.
3.1.4 Compliance RequirementsCompute 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.
Plan and Design
Designing the AI system, defining requirements, and planning development
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Map
Identifying and documenting AI risks, contexts, and impacts
