Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts
(``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances …

In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between
human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a …

Debugging a machine learning model is hard since the bug usually involves the training
data and the learning process. This becomes even harder for an opaque deep learning …

Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the
same performance as black-box models without compromising transparency. ProtoPNets …

We present a novel rationale-centric framework with human-in-the-loop--Rationales-centric
Double-robustness Learning (RDL)--to boost model out-of-distribution performance in few …

We tackle sequential learning under label noise in applications where a human supervisor
can be queried to relabel suspicious examples. Existing approaches are flawed, in that they …

Background Human-in-the-loop research predominantly examines the interaction types and
effects. A more structural and pragmatic exploration of humans and Artificial Intelligence or …

As the performance and complexity of machine learning models have grown significantly
over the last years, there has been an increasing need to develop methodologies to …

As the field of explainable AI (XAI) is maturing, calls for interactive explanations for (the
outputs of) AI models are growing, but the state-of-the-art predominantly focuses on static …