Convex optimization for actionable \& plausible counterfactual explanations

TL;DR

This paper introduces a convex optimization approach to generate counterfactual explanations that are both actionable and plausible, addressing limitations of previous methods that overlooked feature dependencies and realism.

Contribution

It extends prior convex modeling techniques by incorporating mechanisms to ensure counterfactual explanations are actionable and plausible, considering feature dependencies.

Findings

Enhanced counterfactual explanations with actionability and plausibility

Addresses feature dependencies in explanation generation

Builds on previous convex modeling methods

Abstract

Transparency is an essential requirement of machine learning based decision making systems that are deployed in real world. Often, transparency of a given system is achieved by providing explanations of the behavior and predictions of the given system. Counterfactual explanations are a prominent instance of particular intuitive explanations of decision making systems. While a lot of different methods for computing counterfactual explanations exist, only very few work (apart from work from the causality domain) considers feature dependencies as well as plausibility which might limit the set of possible counterfactual explanations. In this work we enhance our previous work on convex modeling for computing counterfactual explanations by a mechanism for ensuring actionability and plausibility of the resulting counterfactual explanations.