ARTIFICIAL INTELLIGENCE FOR SOCIAL EQUALITY: REDEFINING BACKWARDNESS AND RESERVATION POLICIES IN INDIA

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed governance, welfare distribution, and public policy design across the globe. In India, where reservation policies have historically functioned as instruments of social justice, the integration of AI offers both opportunity and risk. This paper examines how AI can contribute to redefining backwardness and reforming reservation policies in India through data-driven and evidence-based frameworks. It argues that while traditional caste-based reservation emerged from constitutional commitments to redress historical injustice, contemporary socio-economic conditions demand more dynamic and multidimensional assessment mechanisms. By employing machine learning models, large-scale socio-economic data analysis, and predictive analytics, policymakers can identify deprivation patterns with greater precision. However, algorithmic bias, data exclusion, and ethical concerns must be carefully addressed to avoid reproducing structural inequalities. The study proposes a hybrid framework combining constitutional safeguards with AI-supported evaluation systems to enhance transparency, equity, and accountability in affirmative action.