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

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 …

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

Learning from class imbalanced datasets poses challenges for many machine learning
algorithms. Many real-world domains are, by definition, class imbalanced by virtue of having …

In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a
user the predictions of a trained decision model by indicating the modifications to be made …

Machine learning (ML) recourse techniques are increasingly used in high-stakes domains,
providing end users with actions to alter ML predictions, but they assume ML developers …

Algorithmic decision-making systems are successfully being adopted in a wide range of
domains for diverse tasks. While the potential benefits of algorithmic decision-making are …

The explanatory capacity of interpretable fuzzy rule-based classifiers is usually limited to
offering explanations for the predicted class only. A lack of potentially useful explanations for …

Counterfactual explanations (CFEs) highlight changes to a model's input that alter its
prediction in a particular way. s have gained considerable traction as a psychologically …