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 …

This article provides a birds-eye view on the role of decision trees in machine learning and
data science over roughly four decades. It sketches the evolution of decision tree research …

There is an emerging interest in generating robust counterfactual explanations that would
remain valid if the model is updated or changed even slightly. Towards finding robust …

The use of counterfactual explanations (CFXs) is an increasingly popular explanation
strategy for machine learning models. However, recent studies have shown that these …

There exist several methods that aim to address the crucial task of understanding the
behaviour of AI/ML models. Arguably, the most popular among them are local explanations …

We study the interplay between counterfactual explanations and model multiplicity in the
context of neural network classifiers. We show that current explanation methods often …

Counterfactual explanations have been widely studied in explainability, with a range of
application dependent methods prominent in fairness, recourse and model understanding …

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 …

Counterfactual explanations shed light on the decisions of black-box models by explaining
how an input can be altered to obtain a favourable decision from the model (eg, when a loan …

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …