The recent surge in publications related to explainable artificial intelligence (XAI) has led to
an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this …

Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

Artificial intelligence (AI) has elicited much attention across disciplines and industries (Hyder
et al., 2019). AI has been defined as “a system's ability to correctly interpret external data, to …

The pairing of massive data sets with processes—or algorithms—written in computer code to
sort through, organize, extract, or mine them has made inroads in almost every major social …

With automation of routine decisions coupled with more intricate and complex information
architecture operating this automation, concerns are increasing about the trustworthiness of …

Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

In the wake of media hype about artificial intelligence (AI)/human collaboration,
organizations are investing considerable resources into developing and using AI. In this …

Background The growing application of artificial intelligence (AI) in healthcare has brought
technological breakthroughs to traditional diagnosis and treatment, but it is accompanied by …

The use of black box algorithms in medicine has raised scholarly concerns due to their
opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and …