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arxiv: 2605.29928 · v2 · pith:G45ZXSGOnew · submitted 2026-05-28 · 💻 cs.HC · cs.AI

Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs

Mahjabin Nahar , Nafis Irtiza Tripto , Aiping Xiong , Ting-Hao 'Kenneth' Huang , Dongwon Lee This is my paper

Pith reviewed 2026-06-29 05:34 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords source biaslogical fallacieshuman evaluationLLM evaluationreasoning judgmentsAI-assisted contenttrust ratings
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The pith

Humans judge logical fallacies more leniently when labeled as human-written than LLMs do.

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

The paper tests whether source labels on statements affect how accurately people and large language models identify logical fallacies. An online experiment assigned 505 participants to evaluate fallacious comments under one of five source conditions, and the same comments were evaluated by three LLMs under matching conditions. Humans gave higher trust and evaluation scores to fallacies labeled human or human with AI assistance. LLM ratings stayed consistent regardless of the attached label. The difference matters in environments where source tags now shape decisions about content quality and moderation.

Core claim

Human evaluators assigned higher trust and evaluation ratings to logically fallacious comments when labeled as written by a human or a human with AI assistance, while LLM evaluations remained stable across source labels including human, AI, human with AI assistance, AI with human assistance, and no disclosure.

What carries the argument

The source label attached to fallacious comments, manipulated across five conditions to isolate its effect on judgment accuracy for humans versus LLMs.

If this is right

  • LLM evaluations of reasoning quality remain consistent when source information is present.
  • Human evaluators require safeguards when source labels indicate human origin.
  • Human-LLM collaboration can offset source bias in fallacy judgment tasks.
  • Stability of evaluations differs across specific LLMs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • LLMs could serve as initial filters before human review in content moderation pipelines.
  • Interventions that hide source labels might bring human detection rates closer to LLM levels.
  • Moderation platforms may gain reliability by routing source-labeled items first through LLMs.

Load-bearing premise

The logical fallacies are equivalent in strength and type across all source label conditions.

What would settle it

A replication study using the same comments where humans show equal fallacy detection rates across all source labels.

Figures

Figures reproduced from arXiv: 2605.29928 by Aiping Xiong, Dongwon Lee, Mahjabin Nahar, Nafis Irtiza Tripto, Ting-Hao 'Kenneth' Huang.

Figure 1
Figure 1. Figure 1: (A) We select news comments with and with [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An overview of the stimuli generation pipeline. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: An overview of the human-subjects study. Par [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average values of perceived logical accu [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Instruction shown to participants before start [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: An example of a stimulus shown to partici [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Attention-check question. (1), Basic (2), Intermediate (3), Advanced (4), Expert (5) • How would you rate your level of expertise with artificial intelligence (AI) tools (e.g., ChatGPT, Gemini, Copilot, Midjourney, etc.) on a 5-point scale? Here, 1 means no expe￾rience and 5 means expert. Answer options. No experience (1), Basic understanding, i.e., familiar with common AI tools or concepts (2), Intermedia… view at source ↗
Figure 10
Figure 10. Figure 10: Debrief information shown to participants at [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Perceived logical accuracy gap = Non-fallacy [PITH_FULL_IMAGE:figures/full_fig_p029_11.png] view at source ↗
read the original abstract

As AI-generated and AI-assisted content floods online spaces, source labels attached to such content can distort human reasoning judgments, with downstream consequences for moderation, evaluation, and decision-making. Whether LLMs share this vulnerability, or offer more source-agnostic evaluation, remains an open question with direct implications for human-AI collaboration. We examine this issue using logical fallacies as a controlled setting to isolate source-label effects on reasoning quality, independent of domain knowledge. We conduct an online study (N=505) where participants are assigned to a source condition (human, AI, human with AI assistance, AI with human assistance, or no disclosure) and evaluate comments containing logical fallacies, comparing their judgments with those of LLMs (GPT-5.2, Gemini 2.5 Flash, Claude Sonnet 4.5), who were evaluated across the same source conditions. Human evaluators were significantly more susceptible to fallacies labeled as written by human or human with AI assistance and assigned higher trust and evaluation ratings in these conditions. LLM evaluations remained comparatively stable across source labels, though performance varied across models. Confidence levels were similarly high across conditions for both humans and LLMs, regardless of fallacy presence. Our findings indicate that source-label bias in reasoning evaluation is primarily a human vulnerability and highlight the potential of human-LLM collaboration in increasingly AI-mediated environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports results from an online study (N=505) in which participants evaluated comments containing logical fallacies under five source-label conditions (human, AI, human with AI assistance, AI with human assistance, no disclosure). Human evaluators showed greater susceptibility to fallacies and assigned higher trust/evaluation ratings when comments were labeled as human-authored or human+AI, while LLM evaluators (GPT-5.2, Gemini 2.5 Flash, Claude Sonnet 4.5) produced more stable judgments across labels. The design uses logical fallacies to isolate source-label effects independent of domain knowledge, with the central claim that source bias is primarily a human vulnerability.

Significance. If the stimulus-equivalence assumption holds and the statistical results are robust, the work provides evidence that LLMs can offer more source-agnostic evaluation than humans in fallacy detection, with implications for moderation pipelines and human-AI collaboration. The empirical comparison across multiple models and the large human sample are strengths.

major comments (2)
  1. [Methods] Methods section: the claim that the design isolates source-label effects 'independent of domain knowledge' rests on the unverified assumption that fallacious comments are equivalent in subtlety, length, and difficulty across the five label conditions. No pre-test equivalence ratings, stimulus counterbalancing procedure, or post-hoc checks for confounds are described; if distinct comments were generated or selected per condition, the reported human bias difference is confounded.
  2. [Results] Results section: the abstract states that humans were 'significantly more susceptible' and assigned 'higher trust and evaluation ratings' but supplies no statistical tests, effect sizes, exclusion criteria, or control variables. Without these details the support for the central human-vs-LLM contrast cannot be evaluated.
minor comments (2)
  1. [Abstract] Abstract and Methods: model version numbers (GPT-5.2, Claude Sonnet 4.5) should be clarified or footnoted if they refer to hypothetical or internal versions rather than publicly released models.
  2. [Results] Figure or table reporting LLM vs. human ratings: axis labels and error bars should be added for direct visual comparison of stability across conditions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the presentation of our methods and results. We address each point below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Methods] Methods section: the claim that the design isolates source-label effects 'independent of domain knowledge' rests on the unverified assumption that fallacious comments are equivalent in subtlety, length, and difficulty across the five label conditions. No pre-test equivalence ratings, stimulus counterbalancing procedure, or post-hoc checks for confounds are described; if distinct comments were generated or selected per condition, the reported human bias difference is confounded.

    Authors: We agree that the manuscript should provide more explicit documentation on stimulus construction. All fallacious comments were generated from a common template set with matched length and fallacy type, then randomly assigned to label conditions. No pre-test equivalence ratings were collected. We will expand the Methods section with a full description of the generation procedure, randomization process, and any available post-hoc length/complexity checks. revision: yes

  2. Referee: [Results] Results section: the abstract states that humans were 'significantly more susceptible' and assigned 'higher trust and evaluation ratings' but supplies no statistical tests, effect sizes, exclusion criteria, or control variables. Without these details the support for the central human-vs-LLM contrast cannot be evaluated.

    Authors: The full Results section reports the relevant statistical tests (including ANOVA and follow-up contrasts), effect sizes, exclusion criteria based on attention checks, and control variables. We acknowledge that the abstract omits these details. We will revise the abstract to include the key statistical results supporting the human-LLM comparison. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical study with no derivation chain

full rationale

The paper reports results from an online experiment (N=505) comparing human and LLM fallacy judgments under source-label conditions. No equations, fitted parameters, predictions, or first-principles derivations are present. Claims rest on direct statistical comparisons of ratings across conditions rather than any internal reduction or self-referential construction. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results occur. The design choice to use logical fallacies as stimuli is a methodological control, not a circular input-output equivalence. This is the expected outcome for an empirical HCI study without theoretical modeling.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical behavioral study with no mathematical derivations, free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5796 in / 930 out tokens · 32600 ms · 2026-06-29T05:34:27.551991+00:00 · methodology

discussion (0)

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Reference graph

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