Saliency maps can explain how deep neural networks classify images. But are they actually
useful for humans? The present systematic review of 68 user studies found that while …

With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research
frequently asserts its relevance to ethical considerations. Yet, the substantiation of these …

This paper examines a kind of explainable AI, centered around what we term pro-hoc
explanations, that is a form of support that consists of offering alternative explanations (one …

Objectives Lack of trust and transparency is stressed as a challenge for clinical
implementation of artificial intelligence (AI). In breast cancer screening, AI-supported …

In this article we provide an analysis focusing on clinical use of two deep learning-based
automatic detection tools in the field of radiology. The value of these technologies conceived …

Objectives Lack of trust and transparency is stressed as a challenge for clinical
implementation of Artificial Intelligence (AI). In breast cancer screening, AI-supported …

Decision-making is increasingly supported by machine recommendations. In healthcare, for
example, a clinical decision support system is used by the physician to find a treatment …

We consider the effectiveness of multi-objective counterfactual explanations (MOCE) in
helping individuals learn tactics, or rules of thumb, to apply when required to select a course …