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 …

Improving customer repurchase intention constitutes a key activity for maintaining
sustainable business performance. Returning customers provide many economic and other …

Due to the increasing use of Machine Learning models in high stakes decision making
settings, it has become increasingly important to have tools to understand how models arrive …

Advancements in mathematical programming have made it possible to efficiently tackle
large-scale real-world problems that were deemed intractable just a few decades ago …

Abstract Data Envelopment Analysis (DEA) allows us to capture the complex relationship
between multiple inputs and outputs in firms and organizations. Unfortunately, managers …

We introduce T-CREx, a novel model-agnostic method for local and global counterfactual
explanation (CE), which summarises recourse options for both individuals and groups in the …

Counterfactual explanations have become a major focus for post-hoc explainability research
in recent years, as they seem to provide good algorithmic recourse solutions, people can …

Counterfactual explanations (CE) are the de facto method for providing insights into black-
box decision-making models by identifying alternative inputs that lead to different outcomes …

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable
machine learning. For a given classifier and an instance classified in an undesired class, its …

As machine learning software increasingly shapes crucial decisions in our daily lives,
ensuring the fairness of these decisions is paramount. Individual fairness guarantees non …