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Response plans, escalation procedures, and incident management for operational emergencies.
Also in Operations & Security
Regularly practicing the implementation of an emergency response plan to stress test the organization's ability to respond to reasonably foreseeable, fast-moving emergency scenarios.
Multiple experts noted that safety drills would be most effective for acute, fast-moving scenarios, particularly those involving critical infrastructure, cybersecurity or chemical, biological, radiological, and nuclear (CBRN) risks. Several mentioned that this approach would be less useful for slower-developing issues like bias and impacts on democratic processes. Several experts highlighted the value of such drills in defining roles, responsibilities, and improving organisational preparedness. Some drew parallels to similar practices in other high-risk industries. Several experts expressed uncertainty about how to define or effectively implement these drills for AI-specific scenarios. A couple mentioned that regulators might lack the necessary expertise to oversee such plans effectively. Some individual experts raised points about the measure being potentially "too little, too late" for certain risks, the importance of including independent oversight, and the need to integrate these drills with broader risk management practices. Several experts suggested that while helpful, this measure alone would not be sufficient to address all types of AI risks and should be part of a more comprehensive approach to AI safety and governance.
Reasoning
Practices implementing emergency response plans to stress-test organizational preparedness for fast-moving scenarios.
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 (multiple stages)
Applies across multiple lifecycle stages
Other
Actor type not captured by the standard categories
Govern
Policies, processes, and accountability structures for AI risk management
