Counterfactual explanations have shown promising results as a post-hoc framework to make
image classifiers more explainable. In this paper, we propose DiME, a method allowing the …

As machine learning methods see greater adoption and implementation in high stakes
applications such as medical image diagnosis, the need for model interpretability and …

Counterfactual explanations, which deal with “why not?” scenarios, can provide insightful
explanations to an AI agent's behavior [Miller [38]]. In this work, we focus on generating …

Multivariate time series are used in many science and engineering domains, including
health-care, astronomy, and high-performance computing. A recent trend is to use machine …

Measuring algorithmic bias is crucial both to assess algorithmic fairness and to guide the
improvement of algorithms. Current bias measurement methods in computer vision are …

Many complex problems are naturally understood in terms of symbolic concepts. For
example, our concept of “cat” is related to our concepts of “ears” and “whiskers” in a non …

Recent advancements in artificial intelligence (AI) technology have raised concerns about
the ethical, moral, and legal safeguards. There is a pressing need to improve metrics for …

Abstract Vision-and-Language Navigation (VLN) is a task where agents must decide how to
move through a 3D environment to reach a goal by grounding natural language instructions …

Explaining deep learning model inferences is a promising venue for scientific
understanding, improving safety, uncovering hidden biases, evaluating fairness, and …

Broad-XAI moves away from interpreting individual decisions based on a single datum and
aims to provide integrated explanations from multiple machine learning algorithms into a …