Employment Shopping -AI-Powered Mobile Hubs for Inclusive Workforce Integration

Abstract

Persistent unemployment disproportionately impacts marginalized populations like ex-offenders, rural workers, and individuals with disabilities. Existing AI-driven job platforms often exacerbate inequalities through access limitations and algorithmic bias. This study introduces Employment Shopping, a novel solution deploying mobile Employment Buses that integrate personalized, bias-mitigating AI job matching with essential on-the-ground human support. By leveraging unique public-private-civic collaborations and focusing on hyperlocalized needs, this hybrid model enhances accessibility and equity where purely digital platforms falter. We evaluate the model's feasibility, ethical considerations (including explainable AI and data privacy for vulnerable groups), and potential impact, aligning with UN Sustainable Development Goals for poverty reduction and equitable labor. Key contributions include a pioneering mobile AI-human hybrid framework for inclusion, a critical analysis of conventional AI job matching limitations, and actionable policy recommendations for truly equitable workforce integration.