Towards Feasible Counterfactual Explanations: A Taxonomy Guided Template-based NLG Method

TL;DR

This paper introduces a taxonomy-guided template-based natural language generation method for counterfactual explanations in AI, improving their feasibility, clarity, and ethical considerations through user studies and domain evaluations.

Contribution

It proposes a novel feature actionability taxonomy and a template-based NLG approach for generating more feasible and comprehensible counterfactual explanations in natural language.

Findings

Higher user ratings for articulation, acceptability, feasibility, and sensitivity.

Significant improvements over existing methods in multiple domains.

Effective integration with existing explainers like DICE, NICE, and DisCERN.

Abstract

Counterfactual Explanations (cf-XAI) describe the smallest changes in feature values necessary to change an outcome from one class to another. However, many cf-XAI methods neglect the feasibility of those changes. In this paper, we introduce a novel approach for presenting cf-XAI in natural language (Natural-XAI), giving careful consideration to actionable and comprehensible aspects while remaining cognizant of immutability and ethical concerns. We present three contributions to this endeavor. Firstly, through a user study, we identify two types of themes present in cf-XAI composed by humans: content-related, focusing on how features and their values are included from both the counterfactual and the query perspectives; and structure-related, focusing on the structure and terminology used for describing necessary value changes. Secondly, we introduce a feature actionability taxonomy with four clearly defined categories, to streamline the explanation presentation process. Using insights from the user study and our taxonomy, we created a generalisable template-based natural language generation (NLG) method compatible with existing explainers like DICE, NICE, and DisCERN, to produce counterfactuals that address the aforementioned limitations of existing approaches. Finally, we conducted a second user study to assess the performance of our taxonomy-guided NLG templates on three domains. Our findings show that the taxonomy-guided Natural-XAI approach (n-XAI^T) received higher user ratings across all dimensions, with significantly improved results in the majority of the domains assessed for articulation, acceptability, feasibility, and sensitivity dimensions.