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Board and executive-level accountability, approval authority, and strategic direction.
Also in Oversight & Accountability
Companies adopt practices typical of high-reliability organizations (HROs), including board risk committees, chief risk officers, multi-party authorization requirements, ethics boards for reviewing development and deployment decisions, and internal audit teams that report directly to the board, tasked with auditing risk management practices.
Many experts viewed this measure positively, seeing it as helpful across various risk areas. Multiple experts emphasised that while important, these structures need to be implemented effectively to have real impact. Several noted that the quality and effectiveness could vary significantly between companies. Several experts highlighted that such governance is a necessary foundation but may be insufficient on its own. Some suggested it should be combined with other measures like strong safety culture or external oversight. Multiple experts pointed out that these structures may be more effective for concrete risks like cybersecurity or chemical, biological, radiological, and nuclear (CBRN) risks; and less so for complex, systemic risks like impacts on democratic processes. Several experts raised concerns about potential downsides, including creating bureaucracy, slowing innovation, or providing a false sense of security. Several were sceptical about real-world effectiveness, with one citing experiences in finance where such structures were often gamed or ineffective. Some individual points raised included: the need for these structures to "have teeth" with robust enforcement, the importance of independence and external reporting capabilities for oversight boards, and the suggestion to adopt a "three lines of defence" model for risk management.
Reasoning
Establishes board-level risk committees and oversight structures with authority over deployment and development decisions.
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
