  Providing explanations about how machine learning algorithms work and/or make
particular predictions is one of the main tools that can be used to improve
their trusworthiness, fairness and robustness. Among the most intuitive type of
explanations are counterfactuals, which are examples that differ from a given
point only in the prediction target and some set of features, presenting which
features need to be changed in the original example to flip the prediction for
that example. However, such counterfactuals can have many different features
than the original example, making their interpretation difficult. In this
paper, we propose to explicitly add a cardinality constraint to counterfactual
generation limiting how many features can be different from the original
example, thus providing more interpretable and easily understantable
counterfactuals.
