Declare and Justify: Explicit assumptions in AI evaluations are necessary for effective regulation (2411.12820v1)

Abstract: As AI systems advance, AI evaluations are becoming an important pillar of regulations for ensuring safety. We argue that such regulation should require developers to explicitly identify and justify key underlying assumptions about evaluations as part of their case for safety. We identify core assumptions in AI evaluations (both for evaluating existing models and forecasting future models), such as comprehensive threat modeling, proxy task validity, and adequate capability elicitation. Many of these assumptions cannot currently be well justified. If regulation is to be based on evaluations, it should require that AI development be halted if evaluations demonstrate unacceptable danger or if these assumptions are inadequately justified. Our presented approach aims to enhance transparency in AI development, offering a practical path towards more effective governance of advanced AI systems.

• The paper argues that explicitly stating and justifying assumptions in AI evaluations is necessary for effective regulation and ensuring the safety of advanced AI systems.
• It identifies core evaluation assumptions including comprehensive threat modeling, proxy task validity, and capability elicitation, highlighting their difficulty in justification.
• Explicit declaration and justification of assumptions, potentially with third-party assessment and regulatory "red/yellow lines", are proposed as essential for transparent AI governance.

Overview of "Declare and Justify: Explicit assumptions in AI evaluations are necessary for effective regulation"

This paper presents a critical analysis of the assumptions inherent in the evaluation protocols of AI systems and highlights their implications for regulatory frameworks. With rapid advancements in AI, the paper posits that evaluations are pivotal in the formulation of safety regulations. It argues for the necessity of developers to explicitly state and justify the assumptions underpinning their evaluation methods to ensure effective governance of AI systems.

Core Assumptions in AI Evaluations

The paper systematically identifies and delineates assumptions intrinsic to AI evaluations. These assumptions encompass threat modeling comprehensiveness, proxy task validity, and the adequate elicitation of capabilities. A complex interplay of these assumptions determines how evaluations infer the safety of AI models. Importantly, many assumptions are not easily justifiable with current methodologies, underscoring potential vulnerabilities in safety evaluations.

1. Comprehensive Threat Modeling: The paper emphasizes the breadth of threat vectors that AI systems might exploit, stressing the difficulty of assessing autonomous capabilities since AI systems may discover undetected threat pathways.
2. Proxy Task Validity: This assumption hinges on the correlation between success on a proxy task and the likelihood of an AI system executing a related dangerous task. The sufficiency of proxy tasks is challenged by the potential for unanticipated task solutions.
3. Capability Elicitation: The paper discusses the evaluators' ability to fully extract an AI system's capabilities. Errors in elicitation could lead to misconceptions about the system’s abilities, particularly when gauging misuse or autonomous threats.

Each of these assumptions carries implications for both existing AI models and those in development. Evaluators face added complexities with forecasting due to the speculative nature of future AI model capabilities and their potential threat vectors.

Implications for Regulation

The discussions within the paper culminate in proposing a regulatory approach where explicit justification and publication of assumptions become mandatory. This would involve third-party assessments of justifications to mitigate risks associated with relying on unjustified evaluation methods. The proposal of transparency is aimed at preventing a false sense of security that might stem from unchallenged evaluation assumptions.

Moreover, the paper highlights potential regulatory measures such as the establishment of "red lines" or "yellow lines," serving as specific thresholds to trigger halts in AI development when evaluations indicate looming dangers.

Future Prospects

While the paper refrains from drawing speculative conclusions, it hints towards a necessary evolution in AI regulatory practices. The expectation is that explicit declaration and justification of assumptions will enrich transparency and allow for more informed judgments regarding the societal deployment of AI technologies. This regulatory necessity is shaped by the intricate nature of AI development methodologies and the unpredictable trajectory of AI capabilities.

Conclusion

The paper underscores the foundational role of evaluations in AI safety regulations, advocating for a structured approach to assumption transparency. It positions these requirements as quintessential to legitimate safety assurances and as critical tools for the effective governance of advanced AI systems. The implications are far-reaching, necessitating changes in how regulators, developers, and evaluators interface to ensure the responsible advancement of AI.