  We predict credit applications with off-the-shelf, interchangeable black-box
classifiers and we explain single predictions with counterfactual explanations.
Counterfactual explanations expose the minimal changes required on the input
data to obtain a different result e.g., approved vs rejected application.
Despite their effectiveness, counterfactuals are mainly designed for changing
an undesired outcome of a prediction i.e. loan rejected. Counterfactuals,
however, can be difficult to interpret, especially when a high number of
features are involved in the explanation. Our contribution is two-fold: i) we
propose positive counterfactuals, i.e. we adapt counterfactual explanations to
also explain accepted loan applications, and ii) we propose two weighting
strategies to generate more interpretable counterfactuals. Experiments on the
HELOC loan applications dataset show that our contribution outperforms the
baseline counterfactual generation strategy, by leading to smaller and hence
more interpretable counterfactuals.
