A Multi-Objective Framework for Human-Centered AIoE Solutions: Bridging the Digital Divide through Optimization

The Artificial Intelligence of Everything (AIoE) is revolutionizing digital infrastructure, yet inequitable access to AI-driven services continues to widen the digital divide. This research proposes a multi-objective optimization framework for human-centered AIoE solutions, ensuring cost efficiency, accessibility, and social fairness. A comparative analysis of metaheuristic algorithms is conducted to optimize AIoE resource allocation. The results demonstrate that one of them, specifically the Greedy Man Optimization Algorithm (GMOA), achieves superior performance, minimizing total cost while maintaining digital equity. A sensitivity analysis reveals that service cost, demand fluctuations, and technology adoption rates significantly impact AIoE optimization. The proposed robust optimization model ensures equitable AIoE deployment, benefiting underserved populations and promoting inclusive digital transformation. Future research should explore hybrid AI-optimization models, integrating machine learning for predictive AIoE decision-making.