Counterfactual explanations (CEs) are advocated as being ideally suited to providing
algorithmic recourse for subjects affected by the predictions of machine learning models …

Model Multiplicity (MM), also known as predictive multiplicity or the Rashomon Effect, refers
to a scenario where multiple, equally performing machine learning (ML) models may be …

AI has become pervasive in recent years, but state-of-the-art approaches predominantly
neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI …

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 …

Abstract Counterfactual Explanations (CEs) have received increasing interest as a major
methodology for explaining neural network classifiers. Usually, CEs for an input-output pair …

We study the problem of assessing the robustness of counterfactual explanations for deep
learning models. We focus on $\textit {plausible model shifts} $ altering model parameters …

The quality of explanations in human-agent interactions is fundamental to the development
of trustworthy AI systems. In this paper we study the problem of generating robust contrastive …

Abstract Counterfactual Explanations (CEs) have emerged as a major paradigm in
explainable AI research, providing recourse recommendations for users affected by the …

Algorithmic modelling relies on limited information in data to extrapolate outcomes for
unseen scenarios, often embedding an element of arbitrariness in its decisions. A …

The increasing use of Machine Learning (ML) models to aid decision-making in high-stakes
industries demands explainability to facilitate trust. Counterfactual Explanations (CEs) are …