Principled Diverse Counterfactuals in Multilinear Models
Abstract
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.
Cite
Text
Papantonis and Belle. "Principled Diverse Counterfactuals in Multilinear Models." Machine Learning, 2024. doi:10.1007/S10994-023-06411-ZMarkdown
[Papantonis and Belle. "Principled Diverse Counterfactuals in Multilinear Models." Machine Learning, 2024.](https://mlanthology.org/mlj/2024/papantonis2024mlj-principled/) doi:10.1007/S10994-023-06411-ZBibTeX
@article{papantonis2024mlj-principled,
title = {{Principled Diverse Counterfactuals in Multilinear Models}},
author = {Papantonis, Ioannis and Belle, Vaishak},
journal = {Machine Learning},
year = {2024},
pages = {1421-1443},
doi = {10.1007/S10994-023-06411-Z},
volume = {113},
url = {https://mlanthology.org/mlj/2024/papantonis2024mlj-principled/}
}