“Mitigating Risks from Rouge AI” by Stephen Clare

Introduction

Misaligned AI systems, which have a tendency to use their capabilities in ways that conflict with the intentions of both developers and users, could cause significant societal harm. Identifying them is seen as increasingly important to inform development and deployment decisions and design mitigation measures. There are concerns, however, that this will prove challenging. For example, misaligned AIs may only reveal harmful behaviors in rare circumstances, or perceive detection attempts as threatening and deploy countermeasures – including deception and sandbagging – to evade them.

For these reasons, developing a range of efforts to detect misaligned behavior, including power-seeking, deception, and sandbagging, among other capabilities, have been proposed. One important indicator, though, has been hiding in plain sight for years. In this post, we identify an underappreciated method that may be both necessary and sufficient to identify misaligned AIs: whether or not they've turned red, i.e. gone rouge.

In [...]

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Outline:

(01:43) Historical Evidence for Rouge AI

(02:59) Recent Empirical Work

(05:18) Potential Countermeasure

(05:22) The EYES Eval

(06:27) EYES Eval Demonstration

(07:40) Future Research Directions

(08:42) Conclusion

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First published:
April 1st, 2025

Source:
https://forum.effectivealtruism.org/posts/uKKoj9iqj2cWKsjrt/mitigating-risks-from-rouge-ai

Narrated by TYPE III AUDIO.

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