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Structured analysis to identify, characterize, and prioritize potential harms and risks.
Also in Risk & Assurance
Understand and assess risks from AI systems
Risk analysis involves evaluating identified risks to understand their likelihood, potential impact, and contributing factors. This process typically includes: - Quantitative analysis: Using statistical methods and models to estimate risk probabilities and impacts. - Causal analysis: Identifying the underlying factors and mechanisms that contribute to risks. - Interdependency analysis: Examining how different risks might interact or compound each other. - Sensitivity analysis: Understanding how changes in assumptions or conditions affect risk assessments.
Reasoning
Analytical evaluation of risk likelihood, impact, and contributing factors through structured assessment methods.
Analysis functions
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.
Plan and Design
Designing the AI system, defining requirements, and planning development
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Measure
Quantifying, testing, and monitoring identified AI risks
