Sustainable Human-AI Collaboration: A Human-Centered Institutional Design for AI-Mediated Institutions

Abstract

ABSTRACT Purpose: This paper examines how institutional design influences the effectiveness and sustainability of Human-AI collaboration in AI-mediated institutions. It argues that sustainable collaboration depends on institutional conditions governing evaluation, integration, governance, and the use of human expertise. Design/Methodology/Approach: The paper adopts a conceptual human-centered systems perspective to examine institutional evaluation mechanisms, incentive structures, hierarchical information filtering, and behavioral normalization loops. Their interaction produces emergent institutional patterns that affect organizational coherence, contextual decision-making, learning, and adaptive capacity. Building on this analysis, HCESF provides the ethical foundation for an AI-mediated institutional design response. Findings: The analysis demonstrates that AI can reinforce organizational weaknesses arising from fragmented communication, misaligned incentives, hidden workload, weakened contextual judgment, and overreliance on measurable indicators. Sustainable Human-AI collaboration therefore requires institutions that enable AI to augment human judgment while preserving contextual knowledge, ethical reasoning, adaptability, and recognition of human contributions. Research Limitations/Implications: The paper is conceptual and does not empirically test the proposed relationships or design principles. Its contribution is to provide an integrated account of the institutional conditions shaping sustainable Human-AI collaboration. Practical Implications: Five AI-mediated institutional design principles are identified: boundary-defined roles, adaptive policy systems, transparent feedback systems, recognition equity mechanisms, and ethical workload governance. Social Implications: Human-centered institutional design can strengthen trust, fairness, resilience, accountability, and human agency while supporting responsible technological advancement. Originality/Value: The paper shifts attention from technological implementation toward the institutional conditions required for sustainable Human-AI collaboration.