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Safety cases, assurance plans, and documented evidence of safety claims.
Also in Risk & Assurance
Disclosing to a regulator how high-stakes decisions regarding model development and deployment are made.
Multiple experts noted that sharing safety cases could enhance transparency, accountability and regulatory oversight. However, several also expressed concerns about the potential for companies to abuse the "proprietary information" exception to avoid full disclosure. Several experts mentioned that this measure could be particularly effective for more concrete risks like chemical, biological, radiological, and nuclear (CBRN) risks; and critical infrastructure disruptions, but less so for complex societal issues like impacts on democratic processes. Some experts worried that safety cases might become a "box-checking exercise" or ineffective bureaucratic requirement without proper enforcement or incentives for thorough evaluation. A couple of experts suggested that safety cases could be valuable if combined with other measures like third-party audits or red-teaming exercises. Individual experts raised various other points, including: the need for a robust safety culture within organisations, the potential for safety cases to incentivise more thorough internal evaluations, and the importance of balancing transparency with protection of proprietary information.
Reasoning
Documented safety case presents evidence of responsible development and deployment decision-making to regulators.
Pre-deployment risk assessments
Comprehensive risk assessments before deployment that would assess reasonably foreseeable misuse and include dangerous capability evaluations that incorporate post-training enhancements and collaborations with domain experts. Risk assessments would inform deployment decisions.
2.2.1 Risk AssessmentThird party pre-deployment model audits
External pre-deployment assessment to provide a judgment on the safety of a model. Auditors, which could be governments or independent third parties, would receive access to a fine-tuning API for testing, or further appropriate technical means.
2.2.3 Auditing & ComplianceExternal assessment of testing procedure
Bringing in external AI evaluation firms before deployment to assess and red-team the company's execution of dangerous capabilities evaluations.
2.2.2 Testing & EvaluationVetted researcher access
Giving good faith, public interest evaluation researchers access to black-box research APIs that provide technical and legal safe harbours to limit barriers imposed by usage policy enforcement, logging, and stringent terms of service.
2.3.1 Deployment ManagementAdvanced model access for vetted external researchers
Examples of advanced access rights could include any of the following: increased control over sampling, access to fine-tuning functionality, the ability to inspect and modify model internals, access to training data, or additional features like stable model versions.
2.2.2 Testing & EvaluationData curation
Careful data curation prior to all development stages (including fine-tuning) to filter out high-risk content and ensure the training data is sufficiently high-quality.
1.1.1 Training DataEffective Mitigations for Systemic Risks from General-Purpose AI
Uuk, Risto; Brouwer, Annemieke; Schreier, Tim; Dreksler, Noemi; Pulignano, Valeria; Bommasani, Rishi (2024)
The systemic risks posed by general-purpose AI models are a growing concern, yet the effectiveness of mitigations remains underexplored. Previous research has proposed frameworks for risk mitigation, but has left gaps in our understanding of the perceived effectiveness of measures for mitigating systemic risks. Our study addresses this gap by evaluating how experts perceive different mitigations that aim to reduce the systemic risks of general-purpose AI models. We surveyed 76 experts whose expertise spans AI safety; critical infrastructure; democratic processes; chemical, biological, radiological, and nuclear risks (CBRN); and discrimination and bias. Among 27 mitigations identified through a literature review, we find that a broad range of risk mitigation measures are perceived as effective in reducing various systemic risks and technically feasible by domain experts. In particular, three mitigation measures stand out: safety incident reports and security information sharing, third-party pre-deployment model audits, and pre-deployment risk assessments. These measures show both the highest expert agreement ratings (>60\%) across all four risk areas and are most frequently selected in experts' preferred combinations of measures (>40\%). The surveyed experts highlighted that external scrutiny, proactive evaluation and transparency are key principles for effective mitigation of systemic risks. We provide policy recommendations for implementing the most promising measures, incorporating the qualitative contributions from experts. These insights should inform regulatory frameworks and industry practices for mitigating the systemic risks associated with general-purpose AI.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Other
Actor type not captured by the standard categories
Govern
Policies, processes, and accountability structures for AI risk management
