Feminist Perspectives on Artificial Intelligence Governance, Facial Recognition, and Racialized Gender Bias

African Journal of Gender and Women Studies | Vol. 11, No. 11, November 2026 | pp. 1–14

DOI: 10.46882/2026/AJGWS/001345

Article Type: Research Article

Title: Feminist Perspectives on Artificial Intelligence Governance, Facial Recognition, and Racialized Gender Bias

Names of Author: Oluwakemi Adebowale¹, Mei-Ling Tan², & Henrik Svensson³

Author's Affiliation: ¹Department of Computer Science and Digital Society, University of Lagos, Lagos, Nigeria; ²Department of Information Systems and Analytics, National University of Singapore, Singapore; ³Department of Informatics, Umeå University, Umeå, Sweden.

Abstract:
Commercial facial recognition software, automated hiring tools, and predictive policing algorithms are increasingly deployed across public and private institutions worldwide without comprehensive gender or racial impact audits. Empirical evaluations consistently demonstrate that machine learning models trained on skewed Western datasets exhibit disproportionately high error rates when identifying dark-skinned women, resulting in wrongful biometric exclusions and discriminatory surveillance. Despite growing global debates on artificial intelligence ethics, African and Asian feminist technologists remain underrepresented in international regulatory bodies. This paper investigates the intersection of algorithmic governance, intersectional bias, and digital rights in Nigeria, Singapore, and Sweden. Grounded in intersectional feminist technology studies, the research combines technical audits of commercial biometric systems with interviews involving 32 AI policy experts and digital rights advocates. We argue for binding algorithmic accountability laws and participatory feminist design standards.

Keywords: Artificial intelligence governance, facial recognition, racialized gender bias, feminist technology studies, algorithmic accountability, digital rights, intersectionality, biometrics

Manuscript Timeline: Received: September 18, 2026; Revised: October 18, 2026; Accepted: October 22, 2026; Published: November 16, 2026.

Citation: Adebowale, O., Tan, M.-L., & Svensson, H. (2026). Feminist Perspectives on Artificial Intelligence Governance, Facial Recognition, and Racialized Gender Bias. African Journal of Gender and Women Studies, 11(11), 1–14. DOI: 10.46882/2026/AJGWS/001345

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