An Interactive Explanatory AI System for Industrial Quality Control

TL;DR

This paper presents an interactive AI system combining explainable knowledge-driven and data-driven methods to improve industrial quality control by providing transparent decisions and integrating human expertise.

Contribution

It introduces a novel human-in-the-loop system that merges inductive logic programming with neural networks for transparent and interactive defect classification.

Findings

System effectively assists experts in defect detection.

Provides transparent explanations for AI decisions.

Reduces workload while maintaining human control.

Abstract

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.