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.
By Aryan Ramchandra Kapadia, Eshwar Chandrasekharan, Koustuv Saha
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...
By Hasibur Rahman, Benjamin R. Cowan, Smit Desai
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.
By Muhammad Salar Khan, Hamza Umer, Hasan Mahmud, Sandra Rothenberg
arXiv:2605. 29928v2 Announce Type: replace-cross Abstract: As AI-generated and AI-assisted content floods online spaces, source labels attached to such content can distort human reasoning judgments, with downstream consequences for moderation, evaluation, and decision-making.
By Mahjabin Nahar, Nafis Irtiza Tripto, Aiping Xiong, Ting-Hao 'Kenneth' Huang, Dongwon Lee
arXiv:2608. 02100v1 Announce Type: cross Abstract: As AI increasingly participates in human decision making, understanding how decision-making authority is distributed between humans and AI has become a fundamental behavioural question.
By Iman Munire Bilal, Yingcan Carol Wang, Ajan Raj, Filippo Giovagnini, Pranav Tewari, Yuwei Zhang, Mei-Chen Zoe Liou, Qamar Zaman
The study investigates whether Large Language Models (LLMs) can translate technical explanations from credit risk models into stakeholder-friendly narratives. Using Freddie Mac loan data, the authors compare standard tabular models (XGBoost + SHAP) with alternative data pipelines (GNN + GNNExplainer and a bimodal mix) and generate explanations with three LLM configurations: a small fine‑tuned Gemma 3 4B, a large fine‑tuned DeepSeek R1 70B, and a zero‑shot Gemini 2.5. Findings show that the quality of explanations is more dependent on the evidence representation than on the LLM, that narratives reliably identify influential factors but are less consistent about the direction of influence, and that credit professionals demand higher evidentiary standards than non‑professionals.
By Sahab Zandi, Noah Kostesku, Christophe Mues, Mar\'ia \'Oskarsd\'ottir, Cristi\'an Bravo
arXiv:2510.08831v2 Announce Type: replace
Abstract: As AI writing tools become widespread, we need to understand how both humans and machines evaluate literary style, a domain where objective standar...
By Wouter Haverals, Meredith Martin
arXiv:2606. 24370v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into decision-support roles in business and policy contexts.
By Hiroshi Okumura
arXiv:2606. 05403v1 Announce Type: new Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions.
By Rohan N. Pradhan, Steve Goley
arXiv:2608.24842v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly deployed as AI analysts to process financial disclosures and support AI-assisted investment decisions....
By Miao Liu, Zhizhe Liu
arXiv:2609.18729v1 Announce Type: cross
Abstract: Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this...
By Lucas G. Uberti-Bona Marin, Thales Bertaglia, Giovanni Astante, Bram Rijsbosch, Gijs van Dijck, Anik\'o Hann\'ak, Gerasimos Spanakis, Konrad Kollnig
arXiv:2609.15624v1 Announce Type: cross
Abstract: Researchers assessing competent generative-AI use at work must choose among self-reports, objective tests, and measures of oversight and reliance. We...
By Daniele Veri'