Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

Artificial intelligence (AI) has huge potential to improve the health and well-being of people,
but adoption in clinical practice is still limited. Lack of transparency is identified as one of the …

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

This article attempts to bridge the gap between widely discussed ethical principles of Human-
centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are …

A surge of interest in explainable AI (XAI) has led to a vast collection of algorithmic work on
the topic. While many recognize the necessity to incorporate explainability features in AI …

Machine learning (ML) models are now routinely deployed in domains ranging from criminal
justice to healthcare. With this newfound ubiquity, ML has moved beyond academia and …

A key challenge in developing and deploying Machine Learning (ML) systems is
understanding their performance across a wide range of inputs. To address this challenge …

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness
of the large-scale impacts of AI systems, and recognition that existing regulations and norms …

As artificial intelligence and machine learning algorithms make further inroads into society,
calls are increasing from multiple stakeholders for these algorithms to explain their outputs …

The remarkable progress in algorithms for machine and deep learning have opened the
doors to new opportunities, and some dark possibilities. However, a bright future awaits …