  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.
