  Counterfactual explanations are a popular type of explanation for making the
outcomes of a decision making system transparent to the user. Counterfactual
explanations tell the user what to do in order to change the outcome of the
system in a desirable way. However, it was recently discovered that the
recommendations of what to do can differ significantly in their complexity
between protected groups of individuals. Providing more difficult
recommendations of actions to one group leads to a disadvantage of this group
compared to other groups.
  In this work we propose a model-agnostic method for computing counterfactual
explanations that do not differ significantly in their complexity between
protected groups.
