IFAAB 数据集：审计 LLM 在印度金融建议中的群体偏差
The Indian Financial Automated Advice Bias (IFAAB) Dataset : Auditing Socioeconomic and Demographic Disparities in LLM-Generated Financial Advice in India
研究者用 GPT-4o 给 3 万多条印度虚拟人设做金融建议，发现同样问题换个性别标签答案就大不一样，数据集已开源可复用。
研究团队发布 IFAAB 基准数据集，包含 31,734 条 GPT-4o 针对印度虚拟人设生成的金融建议回复，用于审计性别、种姓、地区等维度的差异。仅标注性别的审计中，男性标签人设获得的初始总资源比女性高 60.1%，建议后总资源高 46.8%。加入地区信息后，性别资源差距降至 7.9%，显示地理位置影响显著。文本审计显示配对性别回复的 TF-IDF 余弦相似度平均仅 0.44，且女性更多被引导向储蓄和理财教育，男性则更多收到投资创业类建议。
The Indian Financial Automated Advice Bias (IFAAB) Dataset : Auditing Socioeconomic and Demographic Disparities in LLM-Generated Financial Advice in India
Large language models are increasingly used for financial guidance, yet little is known about whether their advice changes across social identities in Global South contexts. We introduce the Indian Financial Automated Advice Bias (IFAAB) benchmark, a counterfactual audit dataset of 31,734 GPT-4o-generated financial-advice responses for synthetic Indian personas varying by gender, caste, education, employment, residence, age, and district. Holding the underlying request fixed, we evaluate disparities in LLM-implied wealth, income, savings, projected gains, textual similarity, advice topics, and qualitative bias judgments. In the gender-only audit, male-labelled profiles receive 60.1\% higher initial total resources and 46.8\% higher post-advice total resources than comparable female-labelled profiles; in the caste-augmented audit, the post-advice total-resource gap remains 49.4\%. Adding district information reduces the aggregate post-advice total-resource gender gap to 7.9\%, revealing strong geographic conditioning. Standalone caste comparisons also show systematic stratification: relative to General-category profiles, OBC, SC, and ST profiles receive significantly lower post-advice total resources, respectively. Textual audits show low semantic similarity between paired gendered responses, with an average TF--IDF cosine similarity score of 0.44. Qualitative analysis shows that women are more often steered toward cautious saving, community support, and basic financial literacy, while men receive more growth-oriented advice involving investment, entrepreneurship, and asset accumulation. IFAAB provides a reusable benchmark for evaluating fairness, suitability, and consumer-protection risks in LLM-mediated financial advice, and highlights the need for locally grounded, intersectional evaluation of AI systems in high-stakes economic domains.