Human–AI Misalignment: A Mechanism-Based Framework for Socio-Technical Breakdown
Keywords:
human–AI misalignment, socio-technical systems, algorithmic decision-making, trust in AI, behavioral responses to AI, organizational performanceAbstract
This article examines the persistent challenges in human–AI collaboration, where advances in artificial intelligence improve decision accuracy yet frequently fail to produce effective socio-technical integration. Existing literature offers fragmented insights into trust, behavior, and system performance, but lacks a coherent, mechanism-based explanation of why collaboration breakdowns occur. To address this gap, the study aims to develop an integrative conceptual framework that explains how discrepancies between AI systems and human actors generate dysfunctional outcomes. Adopting a socio-technical and mechanism-driven analytical approach, the article introduces the concept of Human–AI Misalignment as a multidimensional construct encompassing cognitive, emotional, agency-related, and meaning-based discrepancies. The framework identifies key antecedents such as algorithmic opacity, task complexity, and AI autonomy, which activate psychological mechanisms including cognitive overload, identity threat, perceived loss of control, and perceived unfairness, ultimately shaping behavioral responses such as algorithm aversion, blind reliance, resistance, and disengagement. The study contributes theoretically by reframing AI integration from adoption to alignment, offering a unified explanation of socio-technical breakdowns, and providing a foundation for future empirical research and human-centered AI system design.
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