Ahmed Olakunle Ogungbade,*, Fagbemi Oluwaseyi Ajibola, Oluwatoyin Ishola
Department of Library and Information Science, Tai Solarin University of Education, Ogun State, Nigeria
Department of Human Anatomy, College of Medicine and Surgery, Federal University Lokoja, Kogi State, Nigeria
Department of Biochemistry, Kaduna State University, Kaduna State, Nigeria
*Corresponding author: olakunleogungbade@yahoo.com
Publication History
Submitted: 07-23-2025 | Accepted: 10-01-2025 | Published: 10-30-2025
Cite as: Ogungbade, A. O., Fagbemi, O. A., & Ishola, O. [2025]. Algorithmic Bias in Information Retrieval Systems on Marginalized Communities’ Access to Healthcare Information and Mitigation Strategies: A Case Study of Birnin Gwari Local Government Area in Kaduna State. International Journal of Scholarly Resources, , 18(1), 50-64
Abstract:
Information Retrieval Systems (IRS) have become critical intermediaries in healthcare information access, yet algorithmic bias embedded within these systems disproportionately affects marginalized communities. This study examines the impact of algorithmic bias in IRS on healthcare information access among marginalized populations in Birnin Gwari Local Government Area, Kaduna State, Nigeria. Through a comprehensive analysis of existing literature and contextual examination of the study area, we identify key manifestations of algorithmic bias, document their impacts on healthcare-seeking behavior, and propose evidence-based mitigation strategies. Our findings reveal that algorithmic bias in IRS creates compounding disadvantages for already marginalized communities, limiting their access to quality healthcare information and exacerbating existing health disparities. We propose a framework of technical, policy, and community-centered interventions tailored to resource-constrained settings.
Keywords: Algorithmic bias, information retrieval systems, healthcare access, digital health equity, marginalized communities, Nigeria
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