01724nas a2200181   4500008004100000245006500041210006500106520107500171653002101246653001801267653004001285653002001325653001701345100003401362700002101396700002001417856010501437       2024                        eng d00aAutomating CNN Neuron Interpretation using Concept Induction0 aAutomating CNN Neuron Interpretation using Concept Induction3 a<p>This paper presents an automation pipeline for interpreting hidden neuron activations in Convolutional Neural Networks (CNNs), a crucial objective of Explainable AI (XAI). Previously, our research group addressed this objective by employing concept induction and semantic reasoning using a concept hierarchy derived from the Wikipedia knowledge graph. However, the process was executed manually, taking several days to complete. In this study, we have fully automated the workflow, achieving consistent results while significantly reducing the execution time. The automation pipeline streamlines model training, data preparation, concept induction, image retrieval, classification, and statistical validation, thereby completely eliminating the manual intervention. This automation enables us to efficiently interpret and validate CNN neuron activations by modifying parameters, such as incorporating a broader range of training images and classes and examining additional concept induction results across various neuron layers using different analytical tools.</p>
10aAutomation in AI10aDeep Learning10aExplainable Artificial Intelligence10aKnowledge Graph10aSemantic Web1 aAkkamahadevi, Samatha, Ereshi1 aDalal, Abhilekha1 aHitzler, Pascal  uhttps://daselab.cs.ksu.edu/publications/automating-cnn-neuron-interpretation-using-concept-induction