  Counterfactual explanations provide individuals with cost-optimal actions
that can alter their labels to desired classes. However, if substantial
instances seek state modification, such individual-centric methods can lead to
new competitions and unanticipated costs. Furthermore, these recommendations,
disregarding the underlying data distribution, may suggest actions that users
perceive as outliers. To address these issues, our work proposes a collective
approach for formulating counterfactual explanations, with an emphasis on
utilizing the current density of the individuals to inform the recommended
actions. Our problem naturally casts as an optimal transport problem.
Leveraging the extensive literature on optimal transport, we illustrate how
this collective method improves upon the desiderata of classical counterfactual
explanations. We support our proposal with numerical simulations, illustrating
the effectiveness of the proposed approach and its relation to classic methods.
