@conference {979,
	title = {Automating CNN Neuron Interpretation using Concept Induction},
	booktitle = {THE 23RD INTERNATIONAL SEMANTIC WEB CONFERENCE, ISWC 2024},
	year = {2024},
	abstract = {<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>
},
	keywords = {Automation in AI, Deep Learning, Explainable Artificial Intelligence, Knowledge Graph, Semantic Web},
	author = {Samatha Ereshi Akkamahadevi and Abhilekha Dalal and Pascal Hitzler}
}
