The study examines how large language models (ChatGPT, Gemini, and Grok) embed religious bias in AI‑generated financial advice. Using 432 simulated advisor‑client interactions across four religious identities and three financial decisions, the authors find that only 12‑18% of advice is unbiased, with Gemini showing the most bias and ChatGPT comparable to Grok. The research identifies structural biases in model design and discursive mechanisms—such as religious anchoring and tone modulation—that vary by scenario, revealing a tension between personalization and neutrality in AI advisory services.
arXiv:2608. 09019v1 Announce Type: cross Abstract: 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.
The study investigates how people evaluate AI-generated financial advice by conducting a randomized vignette experiment with 285 U.S. adults. Participants were presented with consistent financial recommendations delivered in three styles—AI, expert, and online community—alongside source labels. The results show that advice style most strongly influenced message and safety appraisals, expert labels increased perceived source knowledge, and decision context shaped risk and safety judgments, with these appraisals explaining a large portion of overall quality, trust, and intended reliance.
arXiv:2609.00999v1 Announce Type: cross Abstract: When a question has valid answers under different normative frameworks, a language model must decide which framework to use and whether it can answer...
arXiv:2608.22852v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific inv...
Demand for personalized financial advising is growing, but consistent advisor expertise is difficult to obtain, scale, and encode in LLM systems. Simple persona prompts rarely specify how a financial advisor should reason and often drift toward generic recommendations.
The paper investigates how user context—such as memory, profiles, and role prompts—affects large language models’ financial analysis. By testing 3,575 SEC filings across twelve LLMs, the study distinguishes between evidence selection and interpretation, finding that interpretation under different roles drives most user-context spillover. Two mitigation strategies—using a user profile instead of an assistant role and separating evidence-based from personalized outputs—reduce but do not eliminate this spillover, with effectiveness varying by model.
The study examines how user context—such as memory, profiles, and role prompts—affects Large Language Models’ (LLMs) financial analysis. Using 3,575 SEC filings and twelve LLMs, the authors distinguish between evidence selection and interpretation, finding that most context spillover arises from differing interpretations under various roles rather than from retrieving different evidence. They evaluate two mitigation strategies—expressing investor mindset as a user profile instead of an assistant role, and separating evidence-based from personalized outputs—both of which reduce but do not eliminate spillover, with effectiveness varying across models.
arXiv:2607. 05680v1 Announce Type: cross Abstract: AI systems are increasingly used to provide legal advice, raising questions about whether laypeople accept guidance from algorithms--especially when that advice is legally correct but socially controversial.
arXiv:2606. 24370v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into decision-support roles in business and policy contexts.
arXiv:2606.29793v3 Announce Type: replace Abstract: Demand for personalized financial advice is growing, yet current LLM-based advisors often fail to provide consistent and specialized guidance.\ Sim...
arXiv:2609.24644v1 Announce Type: cross Abstract: As people turn to generative AI for financial advice, these systems can personalize how they communicate and what they say. Whether these forms of pe...
arXiv:2607. 00856v1 Announce Type: cross Abstract: In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications.