  More and more processes governing our lives use in some part an automatic
decision step, where -- based on a feature vector derived from an applicant --
an algorithm has the decision power over the final outcome. Here we present a
simple idea which gives some of the power back to the applicant by providing
her with alternatives which would make the decision algorithm decide
differently. It is based on a formalization reminiscent of methods used for
evasion attacks, and consists in enumerating the subspaces where the
classifiers decides the desired output. This has been implemented for the
specific case of decision forests (ensemble methods based on decision trees),
mapping the problem to an iterative version of enumerating $k$-cliques.
