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The Indian Financial Automated Advice Bias (IFAAB) Dataset : Auditing Socioeconomic and Demographic Disparities in LLM-Generated Financial Advice in India — arXiv:2610.38239

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.38239
License (as archived): CC0-1.0
Archived: 2026-10-31T06:11:24.973Z
SHA-256 of the archived text: 3f27a7b46ce008701cfb0036e15d1dd6998b7965f2c75b8c56d24a3ac6f72fe4
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How to cite

The Indian Financial Automated Advice Bias (IFAAB) Dataset : Auditing Socioeconomic and Demographic Disparities in LLM-Generated Financial Advice in India — arXiv:2610.38239 — arXiv (en), https://arxiv.org/abs/2610.38239 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_61fa3dc5bbf0 (Metraipolis archive dc_61fa3dc5bbf0, sha256 3f27a7b46ce008701cfb0036e15d1dd6998b7965f2c75b8c56d24a3ac6f72fe4)

BibTeX

@misc{dc61fa3dc5bbf0,
  title = {The Indian Financial Automated Advice Bias (IFAAB) Dataset : Auditing Socioeconomic and Demographic Disparities in LLM-Generated Financial Advice in India — arXiv:2610.38239},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.38239},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_61fa3dc5bbf0; sha256 3f27a7b46ce008701cfb0036e15d1dd6998b7965f2c75b8c56d24a3ac6f72fe4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.38239. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.38239 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T06:07:08.637Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).