AI增强情绪表达会削弱人类识别逻辑谬误的能力。
Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection
- 用大模型系统性地在谬误中加入情绪元素，保持逻辑结构不变。
- 情绪化后的谬误使人类检测准确率下降14.5%（F1值）。
- 愉悦感比恐惧或悲伤更利于识别谬误，且更易让人信服。
逻辑谬误在公共传播中常见，可能误导受众；尽管缺乏合理性，谬误仍可能具有说服力，因为说服力本身具有主观性。我们首次开展计算研究，探讨情绪框架如何影响谬误与说服力，利用大语言模型（LLMs）系统性地在谬误论点中注入情感诉求。我们在八种LLMs上评估其在保持逻辑结构前提下注入情绪的能力，并选取表现最优的模型生成用于人类实验的刺激材料。结果显示，由LLM驱动的情绪化处理使人类对谬误的检测能力平均下降14.5%（F1值）。当人感知到愉悦情绪时，谬误识别效果优于恐惧或悲伤情绪，且这三种情绪状态均显著提升说服力，高于中性或其他情绪状态。本研究揭示了在谬误论证中，由AI驱动的情绪操控可能带来的潜在风险。
原文摘要 · Abstract (English)
Logical fallacies are common in public communication and can mislead audiences; fallacious arguments may still appear convincing despite lacking soundness, because convincingness is inherently subjective. We present the first computational study of how emotional framing interacts with fallacies and convincingness, using large language models (LLMs) to systematically change emotional appeals in fallacious arguments. We benchmark eight LLMs on injecting emotional appeal into fallacious arguments while preserving their logical structures, then use the best models to generate stimuli for a human study. Our results show that LLM-driven emotional framing reduces human fallacy detection in F1 by 14.5% on average. Humans perform better in fallacy detection when perceiving enjoyment than fear or sadness, and these three emotions also correlate with significantly higher convincingness compared to neutral or other emotion states. Our work has implications for AI-driven emotional manipulation in the context of fallacious argumentation.
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