Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

AI systems are adopted in numerous domains due to their increasingly strong predictive
performance. However, in high-stakes domains such as criminal justice and healthcare, full …

As AI-powered systems increasingly mediate consequential decision-making, their
explainability is critical for end-users to take informed and accountable actions. Explanations …

As AI systems demonstrate increasingly strong predictive performance, their adoption has
grown in numerous domains. However, in high-stakes domains such as criminal justice and …

This paper contributes to the growing literature in empirical evaluation of explainable AI
(XAI) methods by presenting a comparison on the effects of a set of established XAI methods …

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

In pathological diagnostics, histological images highlight the oncological features of excised
specimens, but they require laborious and costly staining procedures. Despite recent …

We investigate whether post-hoc model explanations are effective for diagnosing model
errors--model debugging. In response to the challenge of explaining a model's prediction, a …

The current literature on AI‐advised decision making—involving explainable AI systems
advising human decision makers—presents a series of inconclusive and confounding …

Interpretability, explainability, and transparency are key issues to introducing artificial
intelligence methods in many critical domains. This is important due to ethical concerns and …