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.
@article{arxiv.2201.06467,
title = {Principled Diverse Counterfactuals in Multilinear Models},
author = {Ioannis Papantonis and Vaishak Belle},
journal= {arXiv preprint arXiv:2201.06467},
year = {2022}
}