InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph

TL;DR

InSight-R is a novel framework that leverages AutoGraph to objectively identify human failure events and assess interface-induced risks, improving reliability analysis in safety-critical systems like nuclear power.

Contribution

It introduces a risk-informed HFE identification method driven by AutoGraph, integrating empirical data and interface design considerations for enhanced human reliability assessment.

Findings

Automated HFE identification based on operational paths.

Enhanced objectivity and interpretability in HRA.

Scalable real-time human reliability assessment.

Abstract

Human reliability remains a critical concern in safety-critical domains such as nuclear power, where operational failures are often linked to human error. While conventional human reliability analysis (HRA) methods have been widely adopted, they rely heavily on expert judgment for identifying human failure events (HFEs) and assigning performance influencing factors (PIFs). This reliance introduces challenges related to reproducibility, subjectivity, and limited integration of interface-level data. In particular, current approaches lack the capacity to rigorously assess how human-machine interface design contributes to operator performance variability and error susceptibility. To address these limitations, this study proposes a framework for risk-informed human failure event identification and interface-induced risk assessment driven by AutoGraph (InSight-R). By linking empirical behavioral data to the interface-embedded knowledge graph (IE-KG) constructed by the automated graph-based execution framework (AutoGraph), the InSight-R framework enables automated HFE identification based on both error-prone and time-deviated operational paths. Furthermore, we discuss the relationship between designer-user conflicts and human error. The results demonstrate that InSight-R not only enhances the objectivity and interpretability of HFE identification but also provides a scalable pathway toward dynamic, real-time human reliability assessment in digitalized control environments. This framework offers actionable insights for interface design optimization and contributes to the advancement of mechanism-driven HRA methodologies.