A Critical Study of Algorithmic Bias and Its Gendered Implications for the Right to Non-Discrimination in AI Systems

Abstract

This study undertakes a critical examination of algorithmic bias and its gendered implications for the right to non-discrimination in artificial intelligence (AI) systems. As Al increasingly mediates access to essential services-ranging from employment recruitment and credit scoring to healthcare delivery and law enforcement-the neutrality of algorithms has come under question. Far from being objective, these systems often replicate and amplify pre-existing social inequalities due to biased training data, opaque decision-making processes, and inadequate accountability frameworks. Women and gender minorities, in particular, face disproportionate risks of exclusion, stereotyping, and systemic disadvantage when algorithmic outcomes reinforce entrenched patterns of discrimination. The research situates algorithmic bias within broader constitutional and human rights frameworks, foregrounding the right to non-discrimination as a foundational guarantee. It interrogates how automated decision-making systems, while celebrated for efficiency and scale. undermine substantive equality by institutionalizing structural barriers under the guise of technological neutrality. Drawing from interdisciplinary literature across law, computer science, and feminist theory, this study identifies key points of legal friction such as the absence of regulatory oversight, the invisibility of algorithmic processes, and the lack of remedies for affected individuals. Comparative insights from international jurisdictions and human rights bodies are employed to highlight both emerging safeguards and persisting gaps. The study further emphasizes the