Value Alignment Verification

Daniel S. Brown, Jordan Schneider, Scott Niekum

As humans interact with autonomous agents to perform increasingly complicated, potentially risky tasks, it is important that humans can verify these agents' trustworthiness and efficiently evaluate their performance and correctness. In this paper we formalize the problem of value alignment verification: how to efficiently test whether the goals and behavior of another agent are aligned with a human's values? We explore several different value alignment verification settings and provide foundational theory regarding value alignment verification. We study alignment verification problems with an idealized human that has an explicit reward function as well as value alignment verification problems where the human has implicit values. Our theoretical and empirical results in both a discrete grid navigation domain and a continuous autonomous driving domain demonstrate that it is possible to synthesize highly efficient and accurate value alignment verification tests for certifying the alignment of autonomous agents.

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