The Effect of Data Poisoning on Counterfactual Explanations
André Artelt Shubham Sharma Freddy Lecué Barbara Hammer
Counterfactual explanations provide a popular method for analyzing the
predictions of black-box systems, and they can offer the opportunity for
computational recourse by suggesting actionable changes on how to change the
input to obtain a different (i.e. more favorable) system output. However,
recent work highlighted their vulnerability to different types of
manipulations. This work studies the vulnerability of counterfactual
explanations to data poisoning. We formalize data poisoning in the context of
counterfactual explanations for increasing the cost of recourse on three
different levels: locally for a single instance, or a sub-group of instances,
or globally for all instances. We demonstrate that state-of-the-art
counterfactual generation methods \& toolboxes are vulnerable to such data
poisoning.