Despite the proliferation of explainable AI (XAI) methods, little is understood about end-
users' explainability needs and behaviors around XAI explanations. To address this gap and …

Abstract Concept-based interpretability methods aim to explain a deep neural network
model's components and predictions using a pre-defined set of semantic concepts. These …

Deep Neural Networks use thousands of mostly incomprehensible features to identify a
single class, a decision no human can follow. We propose an interpretable sparse and low …

This comprehensive review of concept-supported interpretation methods in Explainable
Artificial Intelligence (XAI) navigates the multifaceted landscape. As machine learning …

Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and
vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate …

Model explanations are very valuable for interpreting and debugging prediction models. We
study a specific kind of global explanations called Concept Explanations, where the goal is …

As AI systems are increasingly transforming our society, it is critical to support relevant
stakeholders to have appropriate understanding and trust in these systems. My dissertation …

Concept-based explanations for convolutional neural networks (CNNs) aim to explain model
behavior and outputs using a pre-defined set of semantic concepts (eg, the model …

Concept-based interpretability methods are a popular form of explanation for deep learning
models which provide explanations in the form of high-level human interpretable concepts …