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
本研究通过随机实验揭示，在AI、专家和在线社区三种建议风格中，风格主导用户对财务建议的评估，专家风格最受信任，且模型可解释高达82.9%的依赖意图，为设计促进理性评估的财务AI提供依据。
AI 中文摘要
随着生成式AI日益被用作个人财务指导的来源，理解人们如何评估此类建议对于支持适当依赖至关重要。我们开展了一项随机化情境实验，涉及285名美国成年人，覆盖八项财务决策，独立变化三种建议风格——AI、专家和在线社区——并在保持底层建议一致的同时显示来源标签。建议风格对信息和安全评估影响最大，专家标签选择性地提高了感知来源知识，而决策情境主要影响风险和安全评估。这些评估与下游判断相关联，模型解释了整体质量69.2%、信任75.9%和预期依赖82.9%的变异。专家风格建议在无来源标签展示时仍最受青睐。我们的发现对理解财务建议评估、区分建议风格与来源标签的作用，以及设计支持基于事实评估而非单纯最大化信任的财务AI具有重要意义。
英文摘要
As generative AI is increasingly used as a source of personal financial guidance, understanding how people appraise such advice is important for supporting appropriate reliance. We conducted a randomized vignette experiment with 285 U.S. adults across eight financial decisions, independently varying three advice styles---AI, expert, and online community---and displayed source labels while holding the underlying recommendation consistent. Advice style most strongly shaped message and safety appraisals, Expert labels selectively increased perceived source knowledge, and decision context primarily shaped risk and safety appraisals. These appraisals were associated with downstream judgments, with models explaining 69.2% of overall quality, 75.9% of trust, and 82.9% of intended reliance. Expert-style advice also remained most preferred when shown without source labels. Our findings have implications for understanding financial advice evaluation, distinguishing the roles of advice style and source labels, and designing financial AI that supports grounded evaluation rather than simply maximizing trust.
