Designing User-Centric Metrics for Evaluation of Counterfactual Explanations

ArXi:2507.15162v2 Announce Type: replace Counterfactual Explanations (CFEs) have grown in popularity as a means of offering actionable guidance by identifying the minimum changes in feature values required to flip an ML model's prediction to something desirable. Unfortunately, most prior research on CFEs relies on artificial evaluation metrics, such as proximity, which may overlook end-user preferences and constraints, e.g., the user's perception of effort needed to make certain feature changes may differ from that of the model designer.