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

2026-08-06

AI Summary

This study addresses the fragmented understanding of sociotechnical risks in human-AI collaboration, which has hindered the identification of common failure mechanisms and effective interventions. To overcome this limitation, the work proposes a unified lifecycle framework encompassing four phases: task allocation, interaction, feedback, and adoption. Through a cross-domain literature review and conceptual modeling, it synthesizes empirical evidence from healthcare, journalism, education, and scientific research to establish the first comprehensive risk taxonomy. The analysis reveals six core risk clusters—including miscalibrated trust, cognitive overload, and responsibility gaps—and elucidates their cascading interdependencies. By moving beyond isolated risk assessments, this research provides a theoretical foundation for resilient human-AI collaboration and informs end-to-end governance strategies and human-centered AI system design.

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.

Problem

• Human-AI collaboration
• Sociotechnical risks
• Cascading failures
• Lifecycle
• Resilience

Innovation

• sociotechnical risks
• cascading failures
• human-AI collaboration
• lifecycle taxonomy
• resilient AI