01734nas a2200145   4500008004100000245009800041210006900139520113700208653002201345653003301367653001901400653002001419100002101439856012801460       2024                        eng d00aUnderstanding CNN Hidden Neuron Activations using Concept Induction over Background Knowledge0 aUnderstanding CNN Hidden Neuron Activations using Concept Induct3 a<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’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>
10aConcept Induction10aConvolutional Neural Network10aExplainable AI10aKnowledge Graph1 aDalal, Abhilehka  uhttps://daselab.cs.ksu.edu/publications/understanding-cnn-hidden-neuron-activations-using-concept-induction-over-background