Artificial intelligence (AI) and machine learning (ML) have recently been radically improved
and are now being employed in almost every application domain to develop automated or …

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 …

Feature attributions and counterfactual explanations are popular approaches to explain a
ML model. The former assigns an importance score to each input feature, while the latter …

Representation learning constructs low-dimensional representations to summarize essential
features of high-dimensional data. This learning problem is often approached by describing …

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite
their importance, these notions have been conceptually underdeveloped and inconsistently …

A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches
have been proposed in the literature to identify bias in machine learning models that are …

Abstract Although standard Machine Learning models are optimized for making predictions
about observations, more and more they are used for making predictions about the results of …

Abstract Machine learning (ML) model interpretability has attracted much attention recently
given the promising performance of ML methods in crash frequency studies. Extracting …

As machine learning has gradually entered into ever more sectors of public and private life,
there has been a growing demand for algorithmic explainability. How can we make the …

Explainable artificial intelligence (XAI) is an important advance in the field of machine
learning to shed light on black box algorithms and thus a promising approach to improving …