Human-Centered Explainability for Industrial Inspection: A Scenario‑Responsive Framework

Abstract

The use of AI-driven vision systems is rapidly becoming a core component of quality assurance in smart‑manufacturing environments. However, the usefulness of these systems depends not only on their detection performance but also on how their outputs are conveyed to operators on the production floor. This study introduces a scenario‑responsive Kansei–XAI interaction framework designed to align explainable visual feedback with human‑centered design attributes such as transparency, confidence building, and operational control. Drawing on insights from two practical industrial contexts, periodic tray inspections and continuous conveyor‑line monitoring, the framework defines a Scenario Rhythm–Risk Profile Matrix together with a risk‑modulated explanation strategy that adjusts the level of explanatory detail according to uncertainty levels and operational hazards. Two graphical interface prototypes demonstrate how the framework can be implemented, and a systematic evaluation methodology is outlined to support future deployment and validation efforts.