Toward Resilient Human-AI Collaboration: A Lifecycle Taxonomy of Sociotechnical Risks and Cascading Failures

Abstract

As AI systems become increasingly integrated into consequential domains such as healthcare, journalism, education, scientific research, organizational decision-making, and defense, effective human-AI collaboration has emerged as a critical challenge. However, the sociotechnical risks that undermine collaboration are often studied in isolation, obscuring the recurring failure mechanisms that cut across domains. This paper presents a lifecycle-oriented synthesis of human-AI collaboration risks spanning four stages: task allocation, interaction, feedback, and adoption. Drawing on evidence from diverse application domains, we identify six recurring cross-domain risk clusters: Trust Miscalibration, Cognitive Burden, Accountability Gap, Capability Erosion, Goal Misalignment, and AI Anxiety and Technostress. We further propose a conceptual interaction model that illustrates how these risks emerge from sociotechnical drivers, interact through cascading pathways, and ultimately affect team performance and human well-being. Our analysis shows that many collaboration failures stem not from isolated technical deficiencies but from interconnected sociotechnical dynamics, helping explain why piecemeal interventions frequently create unintended consequences. By synthesizing fragmented literature into a unified framework, this work provides a foundation for future empirical research, lifecycle-oriented governance, and the design of more resilient, trustworthy, and human-centered human-AI collaboration systems.