Counterfactual Explanations With Multiple Properties in Credit Scoring

Abstract

EXplainable Artificial Intelligence (XAI) aims to reveal the reasons behind predictions from non-transparent classifiers. Explanations of automated decisions are important in critical domains such as finance, legal, and health. As a result, researchers and practitioners in recent years have actively worked on developing techniques that explain decisions from machine learning algorithms. For instance, an explanation technique called counterfactual explanation has recently been gaining traction in XAI. The interest in counterfactual explanations stems from the ability of the explanations to reveal what could have been different to achieve a desired outcome, as opposed to only highlighting important features. For instance, if a customer’s loan application is denied by the bank, a counterfactual will indicate the changes required for the customer to qualify for the loan in the future. For a counterfactual to be considered effective, several counterfactual properties must hold. This paper proposes a novel optimization formulation designed to generate counterfactual explanations that possess multiple properties concurrently. The efficacy of the proposed method is assessed on a publicly available credit dataset. The results showed a trade-off between validity and sparsity, which are both parts of a suite of counterfactual properties. Furthermore, the results showed that our proposed approach compromises validity to some degree but strikes a good balance between validity and sparsity.