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 …

Abstract Concept Bottleneck Models (CBMs) ground image classification on human-
understandable concepts to allow for interpretable model decisions as well as human …

Concept-based learning improves a deep learning model's interpretability by explaining its
predictions via human-understandable concepts. Deep learning models trained under this …

Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method
whose final prediction is based on intermediate, human-understandable concepts rather …

The focus of recent research has shifted from merely increasing the Deep Neural Networks
(DNNs) performance in various tasks to DNNs, which are more interpretable to humans. The …

Advancements in deep learning techniques have given a boost to the performance of
anomaly detection. However, real-world and safety-critical applications demand a level of …

In fields requiring high accountability, it is necessary to understand how deep-learning
models make decisions when analyzing the causes of image classification. Concept-based …

Recent advances in reinforcement learning (RL) have predominantly leveraged neural
network-based policies for decision-making, yet these models often lack interpretability …

Human feedback plays a critical role in learning and refining reward models for text-to-
image generation, but the optimal form the feedback should take for learning an accurate …

Deep neural networks trained via empirical risk minimisation often exhibit significant
performance disparities across groups, particularly when group and task labels are …