  EXplainable AI (XAI) constitutes a popular method to analyze the reasoning of
AI systems by explaining their decision-making, e.g. providing a counterfactual
explanation of how to achieve recourse. However, in cases such as unexpected
explanations, the user might be interested in learning about the cause of this
explanation -- e.g. properties of the utilized training data that are
responsible for the observed explanation. Under the umbrella of data valuation,
first approaches have been proposed that estimate the influence of data samples
on a given model. In this work, we take a slightly different stance, as we are
interested in the influence of single samples on a model explanation rather
than the model itself. Hence, we propose the novel problem of identifying
training data samples that have a high influence on a given explanation (or
related quantity) and investigate the particular case of differences in the
cost of the recourse between protected groups. For this, we propose an
algorithm that identifies such influential training samples.
