Harnessing Artificial Intelligence for Urban Food Redistribution: A Socio-Technical Analysis of the Feeding America Initiative

Abstract: This article examines the potential of an innovative AI-driven food redistribution system, "Feeding America," in addressing the dual challenges of food waste and food insecurity among homeless populations in urban areas. The proposed system integrates deep learning models, self-driving vehicles, and strategically placed vending food hubs to efficiently collect and distribute surplus food from restaurants and stores to those in need. By analyzing the system's architecture, workflow, and implementation strategy, this paper explores how artificial intelligence and autonomous technology can optimize the logistics of food redistribution. The article also investigates the economic incentives, including tax benefits and corporate social responsibility opportunities, that could drive business participation in such initiatives. Through a comprehensive evaluation of potential social, environmental, and economic impacts, as well as anticipated challenges, this article contributes to the growing body of literature on technology-driven solutions for social issues. The findings suggest that AI-enabled food redistribution systems have the potential to significantly reduce food waste, alleviate food insecurity among homeless populations, and create a sustainable model of corporate philanthropy, while also highlighting important considerations for policymakers and future research directions in this field.