Algorithmic Fairness Perceptions in the Global South: Evidence from Bangladesh on Ride-Sharing, Beauty Filters, and Large Language Models

[2508.05281] Algorithmic Fairness Perceptions in the Global South: Evidence from Bangladesh on Ride-Sharing, Beauty Filters, and Large Language Models Skip to main content Search Submit Donate Log in Computer Science > Human-Computer Interaction arXiv:2508.05281 (cs) [Submitted on 7 Aug 2025 ( v1 ), last revised 28 Aug 2026 (this version, v3)] Title: Algorithmic Fairness Perceptions in the Global South: Evidence from Bangladesh on Ride-Sharing, Beauty Filters, and Large Language Models Authors: Ahmed Abdal Shafi Rasel , Ahmed Mustafa Amlan , Tasmim Shajahan Mim View PDF HTML (experimental) Abstract: Algorithmic fairness research comes almost entirely out of North America and Western Europe, so we know little about how people elsewhere judge the algorithms they already rely on every day. We asked people in Bangladesh directly: a bilingual (Bangla and English) survey of 199 participants rated fairness across three everyday scenarios -- ride-sharing prices that shift with context, AI beauty filters that reshape appearance, and large language models that handle cultural values differently than a human would. Four patterns stood out. Context changes the verdict even when the outcome doesn't: a 20% price surge during a medical emergency feels less fair than the identical surge on a casual trip (2.00 vs. 2.17 on a 5-point scale, Wilcoxon p = .006), a small effect uneven across income groups (largest among middle-income participants). People already view surge pricing critically in general; context sharpens the judgment rather than creating it. Demand for transparency, consent, and real user control is close to universal: 85.7% to 90.3% of participants want these protections regardless of gender, income, or prior awareness of algorithmic bias. Beauty-filter harm tracks with self-image more than with social pressure people can easily name; feeling personally affected predicts reduced self-confidence tightly (R^2 = .30, p < .001), though the scale's direction is inferred from context rather than guaranteed by labeled anchors. And among participants who noticed specific instances of LLM cultural bias, many could not say why when asked to elaborate -- recognizing bias and being able to articulate it are different skills. Outcome-only fairness metrics would miss every one of these patterns. Comments: 34 pages Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2508.05281 [cs.HC] (or arXiv:2508.05281v3 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2508.05281 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ahmed Abdal Shafi Rasel [ view email ] [v1] Thu, 7 Aug 2025 11:25:44 UTC (29 KB) [v2] Thu, 27 Aug 2026 12:29:21 UTC (175 KB) [v3] Fri, 28 Aug 2026 08:40:26 UTC (175 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.HC < prev | next > new | recent | 2025-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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