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 …

Algorithmic recourse seeks to provide actionable recommendations for individuals to
overcome unfavorable classification outcomes from automated decision-making systems …

Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

As machine learning models are increasingly being employed to make consequential
decisions in real-world settings, it becomes critical to ensure that individuals who are …

Counterfactual explanations are a prominent example of post-hoc interpretability methods in
the explainable Artificial Intelligence (AI) research domain. Differently from other explanation …

Counterfactual explanations (CEs) are a powerful means for understanding how decisions
made by algorithms can be changed. Researchers have proposed a number of desiderata …

Black-box machine learning models are used in an increasing number of high-stakes
domains, and this creates a growing need for Explainable AI (XAI). However, the use of XAI …

Algorithmic recourse discloses the internal procedures of a black-box decision process
where decisions have significant consequences by providing recommendations to empower …

Counterfactual explanations (CEs) play an important role in detecting bias and improving
the explainability of data-driven classification models. A CE is a minimal perturbed data …