Multi-Objective Counterfactual Explanations

• URL: http://arxiv.org/abs/2004.11165v2
• Date: Wed, 24 Jun 2020 10:03:01 GMT
• Title: Multi-Objective Counterfactual Explanations
• Authors: Susanne Dandl, Christoph Molnar, Martin Binder and Bernd Bischl
• Abstract summary: 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.
• Score: 0.7349727826230864
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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