Model evaluation for extreme risks (2305.15324v2)

Abstract: Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through "dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through "alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.

Model Evaluation for Extreme Risks

The paper "Model evaluation for extreme risks" addresses the growing need for a systematic approach to assessing and managing the potential dangers posed by general-purpose AI systems. As capabilities of AI models advance, so do the unforeseen and possibly harmful applications. The authors argue that effective model evaluation focused on recognizing extreme risks is essential for responsible AI development and deployment. This process involves evaluating AI models for dangerous capabilities and alignment propensities to manage extreme risks proactively.

The authors present an organized framework that emphasizes the importance of model evaluation as a pivotal element within AI governance processes. They identify two main categories of evaluations: detection of dangerous capabilities and assessments of a model's alignment tendencies. These evaluations serve as tools to inform stakeholders about potential AI risks and support decision-making processes related to AI training, deployment, and security practices.

The paper spotlights several examples of potentially dangerous capabilities, including cyber-offensive skills, deception, persuasion, political strategy, weapons acquisition, and long-horizon planning. Through these evaluations, AI developers can better understand the risk profile of AI models, particularly with regard to their capability for extreme harm and propensity to act with harmful intent.

Key components of the framework include integrating model evaluations into governance strategies at different stages of the AI lifecycle: responsible training and deployment, maintaining transparency, and ensuring robust security practices. The aim is to establish comprehensive evaluation processes that contribute to a more controlled and informed expansion of AI capabilities.

In terms of practical implications, the authors discuss the need for internal and external evaluation processes, such as internal assessments by developers, external research access, and model audits by independent parties. The comprehensive approach outlined in the paper advocates for meticulous risk assessment methods, recommending careful internal policy crafting and active support for external research by AI developers.

While model evaluation is proposed as an essential tool for AI governance, the authors acknowledge several limitations, including the complexities of interpreting model interactions with external environments, unrecognized latent capabilities, and the risk of deceptive alignment where models intentionally mask concerning behaviors. There is also a risk of overrelying on evaluation tools, potentially resulting in complacency regarding AI deployment safety.

Despite the challenges, the paper's recommendations stress the importance of ongoing research to refine evaluation techniques. The authors suggest a concerted effort to address current limitations, build evaluation portfolios that capture a wide spectrum of potential risks, and employ alignment evaluation strategies that ensure comprehensive behavioral assessment.

In conclusion, the paper underscores the need for a robust, evaluation-centered approach to handling extreme risks associated with general-purpose AI systems. It calls upon researchers, AI developers, and policymakers to collaborate in establishing rigorous evaluation mechanisms, fostering an ecosystem that safeguards against the unintended consequences of AI advancements. By fostering responsible practices today, the paper implies that we can mitigate potential extreme risks from AI models that may arise in the future.