An Interactive Explanatory AI System for Industrial Quality Control
This addresses the need for transparency and comprehensibility in critical industrial settings, though it appears incremental as it extends existing methods with human interaction.
The paper tackles the problem of defect detection in industrial quality control by proposing an interactive human-in-the-loop system that combines explainable knowledge-driven and data-driven methods, resulting in a system that assists domain experts with transparent explanations and reduces their workload.
Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisions are crucial. Therefore, we aim to extend the defect detection task towards an interactive human-in-the-loop approach that allows us to integrate rich background knowledge and the inference of complex relationships going beyond traditional purely data-driven approaches. We propose an approach for an interactive support system for classifications in an industrial quality control setting that combines the advantages of both (explainable) knowledge-driven and data-driven machine learning methods, in particular inductive logic programming and convolutional neural networks, with human expertise and control. The resulting system can assist domain experts with decisions, provide transparent explanations for results, and integrate feedback from users; thus reducing workload for humans while both respecting their expertise and without removing their agency or accountability.