A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models

Abstract

Human error remains a dominant risk driver in safety-critical sectors such as nuclear power, aviation, and healthcare, where seemingly minor mistakes can cascade into catastrophic outcomes. Although decades of research have produced a rich repertoire of mitigation techniques, persistent limitations: scarce high-quality data, algorithmic opacity, and residual reliance on expert judgment, continue to constrain progress. This review synthesizes recent advances at the intersection of risk-informed decision making, human reliability assessment (HRA), artificial intelligence (AI), and cognitive science to clarify how their convergence can curb human-error risk. We first categorize the principal forms of human error observed in complex sociotechnical environments and outline their quantitative impact on system reliability. Next, we examine risk-informed frameworks that embed HRA within probabilistic and data-driven methodologies, highlighting successes and gaps. We then survey cognitive and human-performance models, detailing how mechanistic accounts of perception, memory, and decision-making enrich error prediction and complement HRA metrics. Building on these foundations, we critically assess AI-enabled techniques for real-time error detection, operator-state estimation, and AI-augmented HRA workflows. Across these strands, a recurring insight emerges: integrating cognitive models with AI-based analytics inside risk-informed HRA pipelines markedly enhances predictive fidelity, yet doing so demands richer datasets, transparent algorithms, and rigorous validation. Finally, we identify promising research directions, coupling resilience engineering concepts with grounded theory, operationalizing the iceberg model of incident causation, and establishing cross-domain data consortia, to foster a multidisciplinary paradigm that elevates human reliability in high-stakes systems.