AI Alignment Strategies from a Risk Perspective: Independent Safety Mechanisms or Shared Failures?
It addresses the problem of AI safety risk mitigation for the AI alignment community by evaluating whether multiple safety techniques provide independent protection or share common vulnerabilities.
The paper analyzes the overlap in failure modes across 7 AI alignment techniques to assess the effectiveness of defense-in-depth strategies, finding that correlated failures reduce redundancy and highlighting implications for risk assessment and research prioritization.
AI alignment research aims to develop techniques to ensure that AI systems do not cause harm. However, every alignment technique has failure modes, which are conditions in which there is a non-negligible chance that the technique fails to provide safety. As a strategy for risk mitigation, the AI safety community has increasingly adopted a defense-in-depth framework: Conceding that there is no single technique which guarantees safety, defense-in-depth consists in having multiple redundant protections against safety failure, such that safety can be maintained even if some protections fail. However, the success of defense-in-depth depends on how (un)correlated failure modes are across alignment techniques. For example, if all techniques had the exact same failure modes, the defense-in-depth approach would provide no additional protection at all. In this paper, we analyze 7 representative alignment techniques and 7 failure modes to understand the extent to which they overlap. We then discuss our results' implications for understanding the current level of risk and how to prioritize AI alignment research in the future.