The Future of Legal Responsibility in Human–AI Collaboration: Revisiting Fault, Intent, and Causation in Hybrid Decision Structures

Abstract

The rapid integration of artificial intelligence into contemporary decision-making processes has fundamentally altered the structure of agency, accountability, and risk within modern legal systems. Human–AI collaboration now characterizes critical domains such as healthcare, finance, transportation, governance, and criminal justice, producing decisions through complex interactions between human judgment and algorithmic inference. This article examines how these hybrid decision structures destabilize the classical foundations of legal responsibility, particularly the doctrines of fault, intent, and causation. Employing a narrative review methodology grounded in descriptive–analytical inquiry, the study synthesizes interdisciplinary scholarship from law, philosophy of action, AI governance, and socio-technical systems theory to reconstruct the conceptual architecture of responsibility under conditions of distributed cognition. The analysis demonstrates that traditional anthropocentric models of responsibility—premised on individual agency, linear causation, and coherent intentionality—are increasingly inadequate for explaining harm and allocating accountability in algorithmically mediated environments. The article proposes a systemic reorientation of legal responsibility, emphasizing shared and layered accountability, institutional governance, and risk-based causation frameworks. By reframing responsibility as a property of socio-technical systems rather than isolated individuals, the study offers a coherent theoretical foundation for adapting liability regimes to the realities of human–AI collaboration. The findings suggest that the future legitimacy and effectiveness of legal systems depend on their capacity to evolve beyond event-based blame toward governance-centered models capable of sustaining accountability amid technological complexity.