1. AI-directed human labour, where AI-directed human labourers drive productivity gains in physical capabilities.
   1. We argue this could increase physical output by 10X within a few years.
2. Fully autonomous robot factories, where AI-directed robots (and other physical actuators) replace human physical labour.
   1. We argue that, with current physical technology and full automation of cognitive labour, this physical infrastructure [...]

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.

1. Alignment without the capacity to follow rules is hopeless. You can’t possibly follow laws like Asimov's Laws (or better alternatives to them) if you can’t reliably learn to abide by simple constraints like the rules of chess.
2. LLMs can’t reliably follow rules. As discussed in Marcus on AI yesterday, per data from Mathieu Acher, even reasoning models like o3 in fact empirically struggle with the rules of chess. And they do this even though they can explicit explain those rules (see same article). The Apple “thinking” paper, which I have discussed extensively in 3 recent articles in my Substack, gives another example, where an LLM can’t play Tower of Hanoi with 9 pegs. (This is not a token-related artifact). Four other papers have shown related failures in compliance with moderately complex rules in the last month.
3. [...]