Multi-Objective Counterfactual Explanations
Abstract
Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of `what-if scenarios'. Most current approaches optimize a collapsed, weighted sum of multiple objectives, which are naturally difficult to balance a-priori. We propose the Multi-Objective Counterfactuals (MOC) method, which translates the counterfactual search into a multi-objective optimization problem. Our approach not only returns a diverse set of counterfactuals with different trade-offs between the proposed objectives, but also maintains diversity in feature space. This enables a more detailed post-hoc analysis to facilitate better understanding and also more options for actionable user responses to change the predicted outcome. Our approach is also model-agnostic and works for numerical and categorical input features. We show the usefulness of MOC in concrete cases and compare our approach with state-of-the-art methods for counterfactual explanations.
Cite
@article{arxiv.2004.11165,
title = {Multi-Objective Counterfactual Explanations},
author = {Susanne Dandl and Christoph Molnar and Martin Binder and Bernd Bischl},
journal= {arXiv preprint arXiv:2004.11165},
year = {2020}
}