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 …

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 models are often used to decide who will receive a loan, a job interview,
or a public benefit. Standard techniques to build these models use features about people but …

The problem of algorithmic recourse has been explored for supervised machine learning
models, to provide more interpretable, transparent and robust outcomes from decision …

We introduce a new framework for generating counterfactual recourse in machine learning
that embraces a “human-in-the-loop" approach by incorporating user preferences …

Machine Learning's proliferation in critical fields such as healthcare, banking, and criminal
justice has motivated the creation of tools which ensure trust and transparency in ML …

Extensive surveys on explanations that are suitable for humans, claims that an explanation
being contrastive is one of its most important traits. A few methods have been proposed to …

Algorithmic recourse recommends a cost-efficient action to a subject to reverse an
unfavorable machine learning classification decision. Most existing methods in the literature …

Automated black-box decision-making models are becoming increasingly pervasive in our
society, but we cannot still understand or act on their recommendations. For example, if a …

The integration of machine learning (ML) models into our daily lives has become ubiquitous,
influencing almost every aspect of our interaction with technology. However, as these …