  Machine learning (ML) applications have automated numerous real-life tasks,
improving both private and public life. However, the black-box nature of many
state-of-the-art models poses the challenge of model verification; how can one
be sure that the algorithm bases its decisions on the proper criteria, or that
it does not discriminate against certain minority groups? In this paper we
propose a way to generate diverse counterfactual explanations from multilinear
models, a broad class which includes Random Forests, as well as Bayesian
Networks.
