Machine learning systems are becoming increasingly ubiquitous. These systems's adoption
has been expanding, accelerating the shift towards a more algorithmic society, meaning that …

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption,
with machine learning systems demonstrating superhuman performance in a significant …

The most successful Machine Learning (ML) systems remain complex black boxes to end-
users, and even experts are often unable to understand the rationale behind their decisions …

In recent times, the progress of machine learning has facilitated the development of decision
support systems that exhibit predictive accuracy, surpassing human capabilities in certain …

Despite the growing body of work in interpretable machine learning, it remains unclear how
to evaluate different explainability methods without resorting to qualitative assessment and …

Interpretable Machine Learning (IML) has become increasingly important in many real-world
applications, such as autonomous cars and medical diagnosis, where explanations are …

Machine learning models have had discernible achievements in a myriad of applications.
However, most of these models are black-boxes, and it is obscure how the decisions are …

There has been a significant surge of interest recently around the concept of explainable
artificial intelligence (XAI), where the goal is to produce an interpretation for a decision made …

Transparency, user trust, and human comprehension are popular ethical motivations for
interpretable machine learning. In support of these goals, researchers evaluate model …

As machine learning systems become ubiquitous, there has been a surge of interest in
interpretable machine learning: systems that provide explanation for their outputs. These …