A Methodological Framework and Questionnaire for Investigating Perceived Algorithmic Fairness
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913978925449216 |
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| author | Rasel, Ahmed Abdal Shafi Amlan, Ahmed Mustafa Mim, Tasmim Shajahan Hasan, Tanvir |
| author_facet | Rasel, Ahmed Abdal Shafi Amlan, Ahmed Mustafa Mim, Tasmim Shajahan Hasan, Tanvir |
| contents | This study explores perceptions of fairness in algorithmic decision-making among users in Bangladesh through a comprehensive mixed-methods approach. By integrating quantitative survey data with qualitative interview insights, we examine how cultural, social, and contextual factors influence users' understanding of fairness, transparency, and accountability in AI systems. Our findings reveal nuanced attitudes toward human oversight, explanation mechanisms, and contestability, highlighting the importance of culturally aware design principles for equitable and trustworthy algorithmic systems. These insights contribute to ongoing discussions on algorithmic fairness by foregrounding perspectives from a non-Western context, thus broadening the global dialogue on ethical AI deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Methodological Framework and Questionnaire for Investigating Perceived Algorithmic Fairness Rasel, Ahmed Abdal Shafi Amlan, Ahmed Mustafa Mim, Tasmim Shajahan Hasan, Tanvir Human-Computer Interaction This study explores perceptions of fairness in algorithmic decision-making among users in Bangladesh through a comprehensive mixed-methods approach. By integrating quantitative survey data with qualitative interview insights, we examine how cultural, social, and contextual factors influence users' understanding of fairness, transparency, and accountability in AI systems. Our findings reveal nuanced attitudes toward human oversight, explanation mechanisms, and contestability, highlighting the importance of culturally aware design principles for equitable and trustworthy algorithmic systems. These insights contribute to ongoing discussions on algorithmic fairness by foregrounding perspectives from a non-Western context, thus broadening the global dialogue on ethical AI deployment. |
| title | A Methodological Framework and Questionnaire for Investigating Perceived Algorithmic Fairness |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.05281 |