DIGITAL INEQUALITY: GENDER-BASED EPISTEMIC BIAS IN ARTIFICIAL INTELLIGENCE SYSTEMS

Abstract

Digital inequality is otherwise referred to as coded inequality. It refers to the hidden or systematic forms of bias and discriminations entrenched in digital techs and algorithms which produce or amplify social inequalities under the guise of neutrality or objectivity. Coded inequality is the latest gender-based bias humanity contends with in Artificial Intelligence systems (AI). AI, long championed as a neutral tool for rational decision-making, increasingly reflects and reinforces gendered inequalities. This paper interrogates the origins, manifestations and ethical implications of gender bias in AI technologies with the critical periscopes of feminism and intersectional epistemologies; it reveals how biased data, algorithmic opacity and gendered labour structures embed patriarchal values into AI systems. The paper adopts the philosophical expository method to analyze typical practices and issues associated with digital techs operations. The expository analysis demonstrates how AI technologies often reproduce social hierarchies under the guise of efficiency. Ethical and epistemological reflections grounded in feminist care ethics, situated knowledge and decolonial feminism challenge the prevailing techno-rationalist paradigm. Finally, the paper outlines a pathway towards feminist AI futures, rooted in participatory design, intersectional data governance, legal reform and inclusive digital literacy. Rather than rejecting AI outright, it calls for its radical transformation through a justice-oriented framework that centers lived experience, accountability and global plurality.