Robust Counterfactual Explanations in Machine Learning: A Survey
Junqi Jiang Francesco Leofante Antonio Rago Francesca Toni
Counterfactual explanations (CEs) are advocated as being ideally suited to
providing algorithmic recourse for subjects affected by the predictions of
machine learning models. While CEs can be beneficial to affected individuals,
recent work has exposed severe issues related to the robustness of
state-of-the-art methods for obtaining CEs. Since a lack of robustness may
compromise the validity of CEs, techniques to mitigate this risk are in order.
In this survey, we review works in the rapidly growing area of robust CEs and
perform an in-depth analysis of the forms of robustness they consider. We also
discuss existing solutions and their limitations, providing a solid foundation
for future developments.