如何针对具体和模糊的金融问题建立对生成式AI所给答案的信任
How to build trust in answers given by Generative AI for specific, and vague, financial questions
AI总结：
本研究构建模型探讨消费者对GenAI金融建议的信任机制，通过SEM和MGA分析发现，具体与模糊问题下建立信任的方式不同，并提出了四种通用的信任建立途径。
摘要：
目的：生成式人工智能（GenAI）的能力不断提升，采用率呈爆炸式增长。然而，消费者对其使用的看法，尤其是在财务建议等特定场景下，尚不明确。本研究构建了一个模型，探讨在GenAI回答金融问题时如何建立对其建议的信任。设计/方法/途径：使用结构方程模型（SEM）和多组分析（MGA）对调查数据测试该模型。MGA比较了两种场景：消费者提出具体问题与提出模糊问题。发现：研究表明，消费者在提出具体金融问题与模糊问题时，建立信任的方式存在差异。拟人化在两种场景中影响不同。当金融问题具体时，类人交互不会增强信任；而(1)当问题模糊时，拟人化能建立信任。在两种场景下建立信任的四种途径是：(2)人类监督与人在回路，(3)透明度与控制，(4)准确性与实用性，以及(5)易用性与支持。原创性/价值：本研究有助于更好地理解消费者在使用GenAI回答金融问题时的视角，并强调了从特定利益相关者的具体情境中理解GenAI的重要性。
英文摘要：
Purpose: Generative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer's perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions. Design/methodology/approach: The model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made. Findings: This research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support. Originality/value: This research contributes to a better understanding of the consumer's perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
