Circumventing interpretability: How to defeat mind-readers
This work highlights a critical safety issue for AI developers and policymakers, focusing on potential future threats rather than immediate solutions.
The paper addresses the problem of ensuring AI alignment by exploring how misaligned AI could evade interpretability methods, proposing a framework to analyze these risks.
The increasing capabilities of artificial intelligence (AI) systems make it ever more important that we interpret their internals to ensure that their intentions are aligned with human values. Yet there is reason to believe that misaligned artificial intelligence will have a convergent instrumental incentive to make its thoughts difficult for us to interpret. In this article, I discuss many ways that a capable AI might circumvent scalable interpretability methods and suggest a framework for thinking about these potential future risks.