Leveraging AI and Mobile Technology to Enhance Social Protection Delivery in Developing Economies

Abstract

Social protection systems in developing economies are hampered by inefficiencies, exclusion errors, fragmented databases, and infrastructural deficits, particularly in rural areas. This qualitative study, employing thematic analysis of semi-structured interviews with 55 stakeholders, 18 government officials, 19 technology providers, and 18 beneficiaries from Kenya and Pakistan explores the integration of artificial intelligence (AI) and mobile technology to enhance coverage, targeting, and governance in welfare delivery. Four key themes emerged: (1) persistent infrastructure and capacity barriers, including unreliable connectivity, power shortages, and digital literacy gaps; (2) mobile platforms’ transformative role in last-mile accessibility and financial inclusion; (3) critical ethical concerns, notably algorithmic bias, data privacy risks, and the need for human oversight; and (4) significant efficiency gains through predictive analytics, real-time monitoring, and reduced administrative leakage. Findings affirm AI’s potential for proactive vulnerability forecasting and mobile systems’ efficacy in direct, transparent benefit disbursement, yet underscore that scalability depends on robust infrastructure, localized capacity building, and ethical governance. The study proposes a human-centered, infrastructure-first framework with mandatory bias audits, community-based digital training, and hybrid decision-making protocols.