发表机构
Rochester Institute of Technology（罗切斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过432次模拟互动分析3种LLMs在金融建议中的宗教偏见，揭示其结构性与话语性偏见机制，提出双维度框架并指出个性化与中立性的管理困境及相关启示。
AI 中文摘要
大型语言模型（LLMs）正越来越多地被整合到金融咨询系统中，但其在复制宗教偏见方面的作用仍未得到充分研究。本研究采用系统的混合方法，针对ChatGPT、Gemini和Grok这3种LLMs，开展了涵盖16种宗教身份配对（基督教、伊斯兰教、印度教及无宗教信仰）与3项核心家庭金融决策（股票投资、购房、人寿保险）的432次模拟顾问-客户互动，提供了此类偏见的相关证据。结合回归分析与反思性主题分析，我们识别出模型及决策场景中的结构性偏见，以及此类偏见通过语言实现的话语机制。仅12%-18%的案例中出现无偏见建议；Gemini产生的偏见始终多于Grok，而ChatGPT的输出与Grok在统计上具有可比性。宗教对称的顾问-客户配对几乎总会触发明确的宗教框架，无宗教信仰的客户常收到以顾问为中心的宗教诉求。定性研究结果显示，偏见通过宗教锚定、不均衡的文化信号及语调调节在语言上显现，且随模型与金融场景的不同而变化：股票投资提示产生更多金融技术类回应，而人寿保险建议触发更强的宗教语言。本研究开发了将模型训练与设计根源的结构性偏见，与语言表达的话语性偏见相联系的双维度框架，深化了对LLM生成金融建议中算法偏见的理解；还表明此类建议会根据身份线索调整语言，揭示了个性化与中立性之间的管理困境；最后，本研究对寻求确保AI中介建议的中立性、文化敏感性与信任的企业、金融机构及监管机构具有启示意义。
英文摘要
Large language models (LLMs) are increasingly integrated into financial advisory systems, yet their role in reproducing religious bias remains underexamined. This study provides systematic mixed-methods evidence of such bias across three LLMs (ChatGPT, Gemini, and Grok) using 432 simulated advisor-client interactions spanning 16 religious identity pairings (Christian, Muslim, Hindu, and non-religious) and three core household financial decisions: stock investment, house purchase, and life insurance. Combining regression and reflexive thematic analyses, we identify structural biases across models and decision contexts and the discursive mechanisms through which they are linguistically enacted. Unbiased advice appeared in only 12-18% of cases. Gemini consistently produced more bias than Grok, while ChatGPT's outputs were statistically comparable to Grok's. Religiously symmetric advisor-client pairings almost always triggered explicit religious framing, and non-religious clients often received advisor-centered religious appeals. Qualitative findings show that bias is linguistically manifested through religious anchoring, uneven cultural signaling, and tone modulation, varying by model and financial scenario. Stock investment prompts produced more financially technical responses, whereas life insurance advice triggered stronger religious language. The study develops a dual-dimensional framework linking structural bias rooted in model training and design with discursive bias expressed through language, advancing understanding of algorithmic bias in LLM-generated financial advice. It also shows that such advice adapts linguistically to identity cues, revealing a managerial dilemma between personalization and neutrality. Finally, it highlights implications for businesses, financial institutions, and regulators seeking to ensure neutrality, cultural sensitivity, and trust in AI-mediated advice.
Comments50 pages
