SOCIOECONOMIC EMPOWERMENT OF MARGINALIZED COMMUNITIES THROUGH AI-DRIVEN INTERVENTIONS

One of the most important issues facing the world today is economic marginality, which demonstrates as restricted market participation, limited access to resources, and ongoing income inequality. Our traditional treatments frequently fall short of offering scalable and long-term solutions that deal with the underlying reasons of marginalisation. The rapid advancement of Artificial Intelligence (AI) presents unprecedented opportunities in bridging the economic gaps through it’s various targeted data-driven, and sustainable interventions. The capacity of artificial intelligence to analyse large databases makes it possible to precisely identify populations that are neglected which in turn makes resource distribution more efficient. Furthermore, by giving marginalised business owners access to digital payment systems, e-commerce channels, and cross-border trade prospects, AI-powered platforms can lower barriers to market entrance. Sustainability needs to be considered from social and environmental perspectives in addition to economic ones. For example, AI-powered agricultural advice systems can encourage climate-resilient farming methods while increasing small-scale farmer’s production. In the same way AI-enabled healthcare solutions can increase workforce readiness by lowering the disease burden in areas that are economically fragile. Artificial intelligence can also improve human agency by providing marginalised individuals with the important resources, networks and information that is actually required to become self-sufficient. This paper further examines the use AI for a diagnostic and prescriptive tool to deal with the issues of economic marginalisation in the heterogeneous population. The structural barriers can be converted into opportunities by incorporating AI into sustainable models that blend the technical innovation with local empowerment tactics. The paper critically assesses the various ethical Issues regarding AI like algorithmic bias, data privacy, and the potential for exacerbating already-existing disparities. This study promotes a base for the intellectual discussion and policy formation as well by providing a multifaceted framework for deploying AI-driven sustainable upliftment models in a range of socioeconomic circumstances.