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Mandated reporting, disclosure obligations, and registration requirements imposed by law.
Also in Legal & Regulatory
Registering upcoming training runs above a certain size with an appropriate state actor. Such reports could include descriptions of architecture, training compute, data collection and curation, training objectives and techniques, and planned risk management procedures.
Multiple experts viewed pre-registration as potentially helpful for improving transparency, allowing government oversight, and enabling proactive risk assessment. However, several also expressed concerns about its limitations. Some noted that pre-registration alone may not directly reduce risks without accompanying enforcement powers or interventions. Several experts mentioned that it could help prepare mitigations and prevent strategic surprises. Several experts questioned the measure's effectiveness for specific systemic risks like bias or democratic process disruption, suggesting these issues are not necessarily tied to model size or training run scale. A couple pointed out that pre-registration might be more relevant for existential risks or "lab accidents" than the listed systemic risks. Concerns raised by individual experts included: potential to discourage experimentation, difficulty in predicting outcomes from training descriptions alone, risk of triggering "race dynamics" if information is made public, and significant compliance costs for companies. One expert worried about the potential for abuse in a "licence to compute" system. Some experts suggested the indirect effects could be valuable, such as improving regulators' knowledge and setting groundwork for further regulation. Several noted the importance of international enforcement for effectiveness.
Reasoning
State-mandated registration requirement for large training runs enabling government oversight and proactive risk assessment.
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.
Plan and Design
Designing the AI system, defining requirements, and planning development
Developer
Entity that creates, trains, or modifies the AI system
Govern
Policies, processes, and accountability structures for AI risk management
