Verification and Validation
Summary
Verification and validation in AI alignment face significant challenges and limitations, particularly when dealing with advanced AI systems. Research indicates that there are fundamental computational and practical barriers to ensuring an AI agent’s behavior meets specific standards. These limitations include the inherent incomputability of determining adherence to behavioral standards, the substantial burdens placed on manual proofs and automated governance systems, and the futility of validating outcomes in the physical world. Additionally, ensuring decidability of behavioral standards necessitates limiting an agent’s capabilities, while layered architectures are deemed inadequate for providing meaningful guarantees. These findings suggest that absolute certainty in AI safety is unattainable, highlighting the need for a more nuanced approach to discussions about general AI safety that acknowledges these inherent limitations and challenges in verification and validation processes.