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.
Context: Chain of Thought Faithfulness
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 [...]
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
First published:
July 2nd, 2025
Source:
https://www.lesswrong.com/posts/me7wFrkEtMbkzXGJt/race-and-gender-bias-as-an-example-of-unfaithful-chain-of)
Narrated by TYPE III AUDIO).
