Summary: We found that LLMs exhibit significant race and gender bias in realistic hiring scenarios, but their chain-of-thought reasoning shows zero evidence of this bias. This serves as a nice example of a 100% unfaithful CoT "in the wild" where the LLM strongly suppresses the unfaithful behavior. We also find that interpretability-based interventions succeeded while prompting failed, suggesting this may be an example of interpretability being the best practical tool for a real world problem.

For context on our paper, the tweet thread is here and the paper is here.

Chain of Thought (CoT) monitoring has emerged as a popular research area in AI safety. The idea is simple - have the AIs reason in English text when solving a problem, and monitor the reasoning for misaligned behavior. For example, OpenAI recently published a paper on using CoT monitoring to detect reward hacking during [...]

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Outline:

(00:49) Context: Chain of Thought Faithfulness

(02:26) Our Results

(04:06) Interpretability as a Practical Tool for Real-World Debiasing

(06:10) Discussion and Related Work

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First published:

Source:

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Narrated by TYPE III AUDIO.