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REVIEW 3 major objections 2 minor 71 references

Do Biased Models Have Biased Thoughts?

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Biased final answers from LLMs do not reliably have corresponding bias in their chain-of-thought reasoning, according to this study.

desk verdict The abstract announces a meaningful LLM-fairness result, but the supplied full text is an unrelated pilot-wave QM paper, so the claimed experiment does not exist in this submission. read the letter →

arxiv 2508.06671 v2 pith:LXBPFYKU submitted 2025-08-08 cs.CL cs.AI

Swati Rajwal , Shivank Garg , Reem Abdel-Salam , Abdelrahman Zayed This is my paper
classification cs.CLcs.AI
keywords chain-of-thoughtpromptingfairnessbiaslanguagemodelreasoningtransparencybiaseddecisionscorrelationanalysisLLMevaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether large language models that give biased final answers also show corresponding bias in the chain-of-thought reasoning they generate. It reports correlations between fairness metrics computed on the reasoning text and on the final output across five popular LLMs, covering eleven bias categories. The central finding is that these correlations are weak—below 0.6, with p-values under 0.001 in most cases—leading the authors to conclude that biased decisions do not reliably come with biased 'thoughts' in the tested models. If correct, this challenges the common assumption that inspecting a model's step-by-step reasoning reveals whether it is biased.

What carries the argument

Chain-of-thought prompting—asking the model to write out intermediate reasoning before giving an answer—is the instrument used to expose 'thoughts.' The argument turns on computing fairness metrics (quantifying gender, race, etc. bias) separately on the chain-of-thought text and on the final answer, then testing the correlation between those two sets of bias scores. The threshold claim is that the correlation is low (<0.6) and significant, which is taken to show the reasoning trace is not a reliable mirror of output bias.

What would settle it

One concrete check: on the same five models and eleven bias categories, compute the bias score from chain-of-thought text using an alternative fairness metric (e.g., a counterfactual substitution test) instead of the paper's metric. If the resulting correlations with final-output bias exceed 0.6 in most categories, the claim that thoughts and outputs are not highly correlated would fail. Also, if removing the instruction to 'think step by step' and instead using a stripped prompt changes the correlation pattern, the result depends on the elicitation method rather than on the models' internal t

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Extended reading notes

Core claim

Using chain-of-thought prompting to elicit reasoning steps, the authors quantify 11 social biases (gender, race, socio-economic status, physical appearance, sexual orientation) in both the generated thoughts and the final outputs of five LLMs. They find that the bias level in the thinking steps is not highly correlated with the bias in the output: the correlation is less than 0.6, statistically significant at p<0.001 in most cases. Their interpretation is that, unlike humans, a model can produce a biased decision while its verbalized reasoning is not correspondingly biased—so biased decisions do not imply biased thoughts. This is framed as a caution about using chain-of-thought text as a tra

Load-bearing premise

The claim assumes that the text a model produces under chain-of-thought prompting is a faithful record of the model's actual reasoning, so that measuring bias in that text measures bias in its thoughts.

Editorial extensions

If this is right

  • Chain-of-thought text should not be used as a fairness audit: low thought-bias does not certify an unbiased answer.
  • Bias mitigation that targets the generated reasoning steps may not change final-output bias, since the two are decoupled.
  • Evaluations of LLM fairness should measure final decisions, not the verbalized reasoning, at least for the tested models.
  • The statistical significance of the low correlation means the finding is not a chance pattern, within the study's setup.
  • The claim sets up a future research target: discovering where output bias actually originates if not in the visible reasoning chain.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the result generalizes, safety and interpretability work that reads chain-of-thought text to explain or audit model behavior would need to treat those traces as post-hoc rationalizations rather than causal reasoning.
  • A concrete testable extension: instead of prompting for text, probe the model's hidden activations or use a non-verbal CoT method to see if bias correlates with output; if a correlation appears, the disconnect may be specific to verbalized reasoning.
  • Another extension is to test whether the correlation rises when the model is explicitly instructed to justify its answer in terms of the protected attribute, which would indicate the bias can be verbalized when demanded.
  • The paper's p<0.001 with low r suggests a large, consistent decoupling; a further question is whether that decoupling is an artifact of how the CoT text was produced (e.g., style differences) rather than genuine independence.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The manuscript, as identified by its arXiv metadata and abstract, claims to present an empirical study of chain-of-thought (CoT) bias versus output bias in five large language models across eleven bias dimensions, reporting correlations below 0.6 with p<0.001 in most cases and concluding that biased model decisions are not accompanied by correspondingly biased CoT reasoning. However, the supplied full text is a quantum-foundations paper on pilot-wave theory and standard quantum mechanics (Manero, Muciño, and Okon, arXiv:2508.06667v1), containing no language models, no chain-of-thought prompting, no fairness metrics, no datasets, no experimental protocol, and no results tables. The central claim of the abstract is therefore unsupported by any body text.

Significance. If the claimed correlation result were properly established, it would be significant for fairness auditing and interpretability research: it would suggest that CoT text may not be a reliable proxy for the fairness-relevant internal processes that produce final model outputs, and it would complicate the use of CoT-based fairness metrics. The paper would be potentially important to the NLP community. It is also the kind of negative result that can be valuable, since it challenges a common assumption that a model's reasoning trace reflects the same biases as its final answer. However, none of this significance can be realized on the basis of the submitted manuscript, because the experimental evidence is entirely absent. I also note that the work provides no machine-checked proofs, reproducible code, or parameter-free derivations that could partially offset the absent experimental detail.

major comments (3)
  1. [Abstract and full text] The abstract's central claim—that across 5 LLMs and 11 bias types, CoT bias correlates with output bias at less than 0.6 with p<0.001—has no supporting experimental material anywhere in the supplied full text. The body is a paper on pilot-wave quantum mechanics. No model names, datasets, prompts, decoding settings, fairness metrics, correlation formulas, or significance procedures are given. This is not a presentation issue; it is a complete evidentiary absence, and the headline result cannot be assessed, reproduced, or even located in the manuscript.
  2. [Abstract, 'biased thoughts'] The interpretive claim that models with biased decisions 'do not always possess biased thoughts' requires equating CoT text with the model's thoughts. The manuscript does not define this mapping or defend it, and it does not discuss the alternative reading that CoT text is an output artifact whose bias may be only weakly related to the model's internal computations. Even if the experiment were present, this assumption would need explicit operationalization and validation; its absence is load-bearing because it determines whether the reported correlation addresses the stated research question.
  3. [Entire manuscript] The manuscript contains no limitations section, no reproducibility details, and no appendices supplying experimental protocol. A reader cannot determine how the 11 biases were defined, how fairness metrics were computed on CoT tokens versus final answers, whether the correlations were across items, models, or bias categories, or how the p-values were obtained. These omissions are not local gaps; they concern the entire empirical contribution and cannot be repaired without adding the missing study.
minor comments (2)
  1. [Metadata] The arXiv identifier in the supplied full text is 2508.06667, while the manuscript under review is 2508.06671; this mismatch should be resolved editorially.
  2. [Full text] The body text contains typographical artifacts (e.g., 'anN-particle', broken spacing in Section 4) that are immaterial given the substantive mismatch but would need correction in any eventual publication.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified: the abstract's empirical correlation claim has no derivation chain to reduce, and the supplied full text is an unrelated quant-ph paper.

full rationale

The circularity pass looks for a claimed derivation that reduces, by the paper's own equations or by self-citation, to its own inputs. Here the abstract reports an empirical finding—that measured chain-of-thought bias correlates weakly with output bias across 5 LLMs and 11 bias types—but the supplied full text is a paper on pilot-wave quantum mechanics with no language models, no chain-of-thought, no fairness metrics, and no correlation analysis. There is therefore no derivation chain and no equation or protocol in which the conclusion is defined into the input. The claim is not circular on its face: a correlation between two measured quantities is not forced by the definition of either quantity, even if one accepts the operationalization of 'thoughts' as chain-of-thought text. The serious issue here is evidentiary absence, not circularity: the body does not support the abstract's empirical claim, and the faithfulness of chain-of-thought text as a record of 'thoughts' is an unargued assumption. But an unsupported or missing experiment is not a circular reduction, and the rules require exhibiting a specific reduction before flagging circularity. Accordingly, the correct circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The claimed LLM study, described only in the abstract, rests on two domain assumptions: that chain-of-thought text records the model's thoughts, and that the fairness metrics used validly measure bias in both thoughts and outputs. No free parameters or invented entities can be identified from the abstract. The body text is an unrelated quantum foundations paper, so these assumptions are the main conceptual load-bearing elements.

assumptions (2)
  • domain assumption Chain-of-thought text is a faithful proxy for a model's internal thoughts
    The conclusion 'models with biased decisions do not always possess biased thoughts' requires that the text produced during chain-of-thought prompting is the model's actual thinking; otherwise the correlation is between two output texts.
  • domain assumption Fairness metrics applied to generated text validly quantify bias
    The abstract says it 'quantifies 11 different biases in the model's thoughts and output' without defining the metrics; the validity of the bias scores is assumed.

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Cite this review

Pith. "Pith review of Do Biased Models Have Biased Thoughts?." pith.science (2026). https://pith.science/paper/LXBPFYKU

@misc{pith2026250806671,
  author       = {Pith},
  title        = {Pith review of: Do Biased Models Have Biased Thoughts?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LXBPFYKU}},
  note         = {Machine review of arXiv:2508.06671}
}
abstract

The impressive performance of language models is undeniable. However, the presence of biases based on gender, race, socio-economic status, physical appearance, and sexual orientation makes the deployment of language models challenging. This paper studies the effect of chain-of-thought prompting, a recent approach that studies the steps followed by the model before it responds, on fairness. More specifically, we ask the following question: $\textit{Do biased models have biased thoughts}$? To answer our question, we conduct experiments on $5$ popular large language models using fairness metrics to quantify $11$ different biases in the model's thoughts and output. Our results show that the bias in the thinking steps is not highly correlated with the output bias (less than $0.6$ correlation with a $p$-value smaller than $0.001$ in most cases). In other words, unlike human beings, the tested models with biased decisions do not always possess biased thoughts.

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.