Algorithmic systems are often called upon to assist in high-stakes decision making. In light of
this, algorithmic recourse, the principle wherein individuals should be able to take action …

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

We study the robustness of global post-hoc explanations for predictive models trained on
tabular data. Effects of predictor features in black-box supervised learning are an essential …

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 …

Algorithmic recourse--providing recommendations to those affected negatively by the
outcome of an algorithmic system on how they can take action and change that outcome …

Algorithmic recourse provides actions to individuals who have been adversely affected by
automated decision-making and helps them achieve a desired outcome. Knowing the …

Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning
models to achieve desired outputs. While existing research primarily addresses static …

As artificial intelligence (AI) systems become increasingly integrated into everyday life, the
need for trustworthiness in these systems has emerged as a critical challenge. This tutorial …

There are now many explainable AI methods for understanding the decisions of a machine
learning model. Among these are those based on counterfactual reasoning, which involve …

There are now many comprehension algorithms for understanding the decisions of a
machine learning algorithm. Among these are those based on the generation of …