Data-Driven Framework for Aligning Artificial Intelligence with Inclusive Development in the Global South

Artificial Intelligence is reshaping social, political, economic, and cultural life, yet its developmental value in the Global South remains contingent on governance, participation, and design choices. This study develops and validates a data-driven framework that aligns Artificial Intelligence with inclusive development across four interdependent dimensions like access, agency, accountability, and adaptation using a mixed-method, sequential explanatory design that integrates large-sample surveys, qualitative interviews and observations, and participatory workshops across six urban, peri-urban, and rural sites (total n=1920). Measurement development followed best practices in item generation, content validity, cognitive interviewing, piloting, and psychometric evaluation; exploratory factor analysis and confirmatory factor analysis supported a four-factor structure with satisfactory reliability and convergent discriminant validity. Structural equation modeling indicated that access and adaptation are the strongest predictors of service reach and time efficiency, whereas agency and accountability are most closely associated with grievance resolution and reductions in reported harms; these relations were strong across subgroups and alternative specifications. Qualitative integration clarified mechanisms that map onto the quantitative signals, including infrastructural precarity that constrains reach, contestability gaps that limit remedy, and locally responsive design features that reduce transaction costs. The framework translates normative commitments into measurable levers for policy and practice: investments that prioritize access and adaptation expand reach and efficiency, while strengthening agency and accountability enhances remedy and safety. Embedding the four dimensions into diagnostics, procurement, audit, and performance management offers a practical pathway to make Artificial Intelligence inclusive by default in diverse low-resource settings.