发表机构
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
通过实验对比AI、专家、同行风格金融建议的评估差异，发现归属标注与沟通线索共同影响评估，错误标注会提升AI建议评分。
AI 中文摘要
随着生成式AI日益成为日常决策（包括金融选择）的常见来源，了解人们如何评估AI生成的金融建议至关重要。我们开展了一项预先注册的小插图实验（样本量N=285），在该实验中，实质性金融内容——包括事实、数值、建议方向和核心推理——保持不变，而沟通风格则在AI金融助手（AI）、注册金融规划师（Expert）和在线社区论坛（OC）的建议之间变化。显示的来源归属通过正确标注、未标注和错误标注条件进行独立操纵，使我们能够将归属效应与特定来源的沟通线索分离开来。在10项结果中，专家建议的评分比AI建议更有利（|d|=0.20至0.47），且在无来源标注的情况下，这一优势依然明显，此时专家建议在10项结果中的8项上优于AI建议（最高d=0.60）。正确标注带来的区分有限，而错误标注会提高AI建议在情境适配性和整体质量上的评分（两者的d均为0.42），并削弱专家在情境适配性上的优势（d=-0.36）。描述性分析进一步显示，AI建议对显示的归属最为敏感，相反，建议风格的差异在AI标注下最为明显。这些发现表明，金融建议的评估受显示的归属和消息层面的沟通线索共同影响。我们将披露定位为一种非中性的透明机制，而是一种解释框架，其准确性以及与消息线索的相互作用可塑造信任和依赖。
英文摘要
As generative AI increasingly becomes a common source of daily decision-making, including financial choices, it is critical to understand how people evaluate AI-generated financial advice. We conducted a preregistered vignette experiment (N = 285) in which substantive financial content---including facts, numerical values, recommendation direction, and core reasoning---was held constant while communication style varied across AI Financial Assistant (AI), Certified Financial Planner (Expert), and Online Community Forum (OC) advice. Displayed source attribution was independently manipulated through correctly labeled, unlabeled, and mislabeled conditions, allowing us to separate attribution effects from source-specific communication cues. Expert advice was rated more favorably than AI advice on 9 of 10 outcomes (|d|=0.20--0.47), and this advantage remained visible without source labels, where Expert advice outperformed AI advice on 8 of 10 outcomes (up to d=0.60). Correct labels added limited differentiation, whereas mislabeling increased ratings of AI advice for situational fit and overall quality (d=0.42 for each) and attenuated the Expert advantage in situational fit (d=-0.36). Descriptive analyses further showed that AI advice was most responsive to displayed attribution and, conversely, that advice-style differences were most visible under an AI label. These findings show that financial-advice evaluations are shaped jointly by displayed attribution and message-level communication cues. We position disclosure not as a neutral transparency mechanism, but as an interpretive frame whose accuracy and interaction with message cues can shape trust and reliance.
