arXiv:2605.29928v2 Announce Type: replace-cross 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 condu
The proliferation of AI-generated content online makes understanding human and AI vulnerability to source cues in reasoning judgments increasingly critical.
Understanding how source labels, particularly 'AI-generated,' influence human and LLM reasoning is crucial for content moderation, evaluation of information, and the development of reliable human-AI collaboration.
This research highlights a potential vulnerability in human reasoning to AI source labels, suggesting a need for mechanisms to promote source-agnostic evaluation in both human and AI systems.
- · AI ethics researchers
- · Content moderation platforms
- · Developers of robust AI systems
- · Platforms reliant on naive human judgment
- · Producers of misleading AI-labeled content
Human decisions informed by AI content may be biased by source labels rather than content quality.
Development of AI systems designed to filter or de-bias information based on source credibility rather than content.
Potential for new forms of information warfare or persuasion tactics leveraging source-cue manipulation in an AI-dominated information landscape.
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Read at arXiv cs.AI