How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations

Abstract

Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, counterfactuals are “actionable” explanations that help users to understand how model decisions can be changed by adapting features of an input. A case-based approach to counterfactual discovery harnesses Nearest-unlike Neighbours as the basis to identify the minimal adaptations needed for outcome change. This paper presents the DisCERN algorithm which uses the query, its NUN and substitution-based adaptation operations to create a counterfactual explanation case. DisCERN uses Integrated Gradients (IntG) feature attribution as adaptation knowledge to order substitution operations and to bring about the desired outcome with as few changes as possible. We present our novel approach with IntG where the NUN is used as the baseline against which the feature attributions are calculated. DisCERN also uses feature attributions to identify a NUN closer to the query, and thereby minimise the total change needed, but results suggest that the number of feature changes can increase. Overall, DisCERN outperforms other counterfactual algorithms such as DiCE and NICE in generating valid counterfactuals with fewer adaptations.