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

Principled Diverse Counterfactuals in Multilinear Models