A Theoretical Ecosystem Framework for Human-Centric AI in Multi-Tier Supply Chains: Aligning Incentives and Value Co-Creation

Modern supply chains, particularly multi-tier supply chains (MTSCs), increasingly grapple with mounting complexity, data fragmentation, and ethical challenges amid the rapid integration of Artificial Intelligence (AI). While AI offers transformative potential for coordination, forecasting, and decision support, conventional AI systems often suffer from opacity, algorithmic bias, and weak alignment with human and organizational values, constraining their real-world effectiveness in distributed and interdependent ecosystems. This paper is motivated by the need to bridge this dual gap, wherein AI’s technical limitations amplify existing vulnerabilities within supply networks, and the structural heterogeneity of MTSCs further impedes responsible AI deployment. The central problem addressed herein is the absence of explainability, ethical grounding, and stakeholder coordination in current AI implementations, which collectively undermine trust, transparency, and equitable outcomes across supply tiers. Addressing these challenges requires moving beyond siloed optimization toward systems that recognize the intertwined human, technological, and institutional dimensions of decision-making. To this end, we propose a comprehensive ecosystem framework that fuses Human-Centric AI (HCAI) principles with the structural and operational realities of MTSCs. Our approach explicitly integrates mechanisms for incentive alignment, participatory governance, and value co-creation, ensuring that AI deployment serves collective rather than isolated interests. The framework articulates how responsible AI can be embedded into supply networks through multi-level design paradigms, encompassing data ethics protocols, human-in-the-loop decision architectures, and adaptive governance layers capable of managing evolving risks and ethical trade-offs. We further contribute a novel architecture that combines AI governance strategies with interdisciplinary collaboration models, bridging technological design, supply chain coordination, and ethical oversight. This architecture demonstrates how aligning stakeholder incentives and enabling participatory AI co-design can mitigate bias propagation, enhance algorithmic accountability, and foster stakeholder trust, key prerequisites for AI adoption in fragmented and globally distributed supply systems. Ultimately, embedding HCAI principles into MTSCs represents a socio-technical transformation, one that strengthens resilience, fairness, and sustainability while providing a blueprint for ecosystem-wide AI governance.