Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …

Abstract Concept Bottleneck Models (CBM) are inherently interpretable models that factor
model decisions into human-readable concepts. They allow people to easily understand …

Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

Deploying AI-powered systems requires trustworthy models supporting effective human
interactions, going beyond raw prediction accuracy. Concept bottleneck models promote …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …

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 …