Intersectional Inequalities in Access to AI-Enhanced Public Services: Gender, Class, Disability, and Region

Abstract

The increasing adoption of artificial intelligence in public service delivery has created new opportunities for efficiency, automation, and targeted welfare provision. However, these benefits are not distributed equally across society. This paper examines intersectional inequalities in access to AI-enhanced public services by focusing on the combined effects of gender, class, disability, and regional location. The study highlights how digital access, algorithmic design, administrative capacity, and user trust shape citizens’ ability to benefit from AI-driven systems in health, education, welfare, identity verification, and social protection services. The findings suggest that marginalized groups often experience layered disadvantages, where low income, rural residence, limited digital literacy, gender-based restrictions, and disability-related barriers interact to reduce access and service outcomes. Women from low-income households, persons with disabilities, and rural citizens were found to face higher risks of exclusion due to limited connectivity, inaccessible platforms, biased data systems, and weak institutional support. The paper argues that AI-enhanced public services can either reduce or deepen inequality depending on how they are designed, implemented, and monitored. Inclusive policy frameworks, participatory design, accessible digital infrastructure, transparency, and human support mechanisms are essential to ensure that AI-enabled governance strengthens equity rather than reproducing existing social divisions.