@proceedings {974,
	title = {Understanding CNN Hidden Neuron Activations using Concept Induction over Background Knowledge},
	journal = {THE 23RD INTERNATIONAL SEMANTIC WEB CONFERENCE, ISWC 2024},
	year = {2024},
	abstract = {<p>A major challenge in Explainable AI is interpreting hidden neuron activations accurately. These in- terpretations can reveal what a deep learning system perceives as relevant in the input data, thereby addressing the black-box nature of such systems. The state of the art indicates that hidden node acti- vations can be interpretable by humans, but there{\textquoteright}s a lack of systematic automated methods to verify these interpretations, especially those that utilize substantial background knowledge and inherently explainable methods. In this proposal, we introduce a novel model-agnostic post-hoc Explainable AI method based on a Wikipedia-derived concept hierarchy with approximately 2 million classes. Our approach utilizes OWL-reasoning-based Concept Induction for explanation generation and compares with off-the-shelf pre-trained multimodal-based explainable methods. Our results demonstrate that our method automatically provides meaningful class expressions as explanations to individual neurons in the dense layer of a Convolutional Neural Network, outperforming prior work in both quantitative and qualitative aspects.</p>
},
	keywords = {Concept Induction, Convolutional Neural Network, Explainable AI, Knowledge Graph},
	author = {Abhilehka Dalal}
}
