Counterfactual explanations are viewed as an effective way to explain machine learning
predictions. This interest is reflected by a relatively young literature with already dozens of …

Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

In this paper we propose a new algorithm, named NICE, to generate counterfactual
explanations for tabular data that specifically takes into account algorithmic requirements …

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 …

Abstract 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 …

There has been recent interest in exploring generative goals for counterfactual reasoning,
such as individualized treatment effect (ITE) estimation. However, existing solutions often fail …

To increase the adoption of counterfactual explanations in practice, several criteria that
these should adhere to have been put forward in the literature. We propose counterfactual …

Due to the increasing use of machine learning in practice it becomes more and more
important to be able to explain the prediction and behavior of machine learning models. An …

Counterfactual explanations have become a mainstay of the XAI field. This particularly
intuitive statement allows the user to understand what small but necessary changes would …

The growing prevalence of artificial decision systems has prompted a keen interest in their
efficiency, yet this progress is accompanied by their inherent complexity. This poses a …