在线医疗信息评估中的诱饵困境：LLM与人类评判者可信度评估的比较研究
The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges
AI总结：
本研究通过众包实验和LLM模拟实验，比较LLM与人类在COVID-19医疗信息可信度评估中的诱饵效应，发现LLM虽更准确但更易受显著诱饵错误信息影响，证实AI智能体存在认知偏差风险，强调去偏和心理学导向AI审计的重要性。
摘要：
AI在自动化信息判断任务中是否会产生认知偏差？尽管近年来在测量和缓解AI及大语言模型（LLMs）中的社会偏见和算法偏见方面取得了进展，但LLMs在多大程度上表现得“理性”，或者它们是否同样容易受到人类认知偏差触发因素的影响，目前尚不清楚。为解决这一开放性问题，本研究由一项众包用户实验和一项基于LLM的模拟实验组成，在信息检索（IR）场景下比较了LLM与人类评判者在潜在诱饵效应下的可信度评估，并以传统人类评估者为基线，实证检验了LLMs在COVID-19医疗（错误）信息评估任务中相对于传统人类评估者的认知偏差程度。来自被试间用户实验和基于LLM的重复实验的结果表明：1）更大、更新的LLMs在区分可信信息与错误信息方面往往表现出更高的一致性和准确性。然而，由于存在更显著的诱饵性错误信息结果，它们更有可能对错误信息给出更高的评分；2）虽然诱饵效应在人类和LLM评估中均有出现，但与人类可信度评分相比，该效应在LLM判断中跨不同条件和主题更为普遍。与通常假设的AI工具“理性”相反，本研究实证确认了LLM智能体中嵌入的认知偏差风险，评估了诱饵对LLMs的影响并与人类可信度评估进行对照，从而凸显了为自动化判断任务及其他场景对AI智能体进行去偏以及开发基于心理学信息的AI审计技术和政策的重要性和复杂性。
英文摘要：
Can AI be cognitively biased in automated information judgment tasks? Despite recent progresses in measuring and mitigating social and algorithmic biases in AI and large language models (LLMs), it is not clear to what extent LLMs behave "rationally", or if they are also vulnerable to human cognitive bias triggers. To address this open problem, our study, consisting of a crowdsourcing user experiment and a LLM-enabled simulation experiment, compared the credibility assessments by LLM and human judges under potential decoy effects in an information retrieval (IR) setting, and empirically examined the extent to which LLMs are cognitively biased in COVID-19 medical (mis)information assessment tasks compared to traditional human assessors as a baseline. The results, collected from a between-subject user experiment and a LLM-enabled replicate experiment, demonstrate that 1) Larger and more recent LLMs tend to show a higher level of consistency and accuracy in distinguishing credible information from misinformation. However, they are more likely to give higher ratings for misinformation due to the presence of a more salient, decoy misinformation result; 2) While decoy effect occurred in both human and LLM assessments, the effect is more prevalent across different conditions and topics in LLM judgments compared to human credibility ratings. In contrast to the generally assumed "rationality" of AI tools, our study empirically confirms the cognitive bias risks embedded in LLM agents, evaluates the decoy impact on LLMs against human credibility assessments, and thereby highlights the complexity and importance of debiasing AI agents and developing psychology-informed AI audit techniques and policies for automated judgment tasks and beyond.
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