Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs
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 strong 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 disc...
| Authors | Mahjabin Nahar, Nafis Irtiza Tripto, Aiping Xiong et al. |
|---|---|
| Primary category | cs.HC |
| Published | 28 May 2026 |
| Last updated at source | 4 September 2026 |
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