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.

Highlights

  • Interpretable machine learning methods have become very important in recent years to explain the behavior of black box machine learning (ML) models

  • We propose the Multi-Objective Counterfactuals (MOC) method, which translates the counterfactual search into a multi-objective optimization problem

  • We introduce Multi-Objective Counterfactuals (MOC), which to the best of our knowledge is the first method to formalize the counterfactual search as a multi-objective optimization problem

Read more

Summary

Introduction

Interpretable machine learning methods have become very important in recent years to explain the behavior of black box machine learning (ML) models. A useful method for explaining single predictions of a model are counterfactual explanations. For people whose credit applications have been rejected, it is valuable to know why they have not been accepted, either to understand the decision making process or to assess their actionable options to change the outcome. Counterfactuals provide these explanations in the form of “if these features had different values, your credit application would have been accepted”. For such explanations to be plausible, they should only suggest small changes in a few features

Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.