Abstract Concept Bottleneck Models (CBMs) have recently been proposed to address the
'black-box'problem of deep neural networks, by first map** images to a human …

In recent years, concept-based approaches have emerged as some of the most promising
explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs) …

Mechanistic interpretability aims to understand how models store representations by
breaking down neural networks into interpretable units. However, the occurrence of …

Research on Explainable Artificial Intelligence has recently started exploring the idea of
producing explanations that, rather than being expressed in terms of low-level features, are …

Recent efforts to understand intermediate representations in deep neural networks have
commonly attempted to label individual neurons and combinations of neurons that make up …

Machine learning is a vital part of many real-world systems, but several concerns remain
about the lack of interpretability, explainability and robustness of black-box AI systems …

Conceptors are versatile neuro-symbolic formalizations of concepts as they arise in neural
networks, with promising results on supervised tasks. However, the use of conceptors in …