Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization

نویسندگان

چکیده

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with perturbation vector features that alters prediction result. Given vector, can interpret it an "action" obtaining one's desired decision In practice, however, showing only often insufficient users execute action. The reason if there asymmetric interaction among features, such causality, total cost action expected depend on order changing features. Therefore, practical CE are required provide appropriate in addition vector. For this purpose, we propose new framework called Ordered (OrdCE). We introduce objective function evaluates pair and based feature interaction. To extract optimal pair, mixed-integer linear optimization approach our function. Numerical experiments real datasets demonstrated effectiveness OrdCE comparison unordered methods.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2021

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v35i13.17376