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

• URL: http://arxiv.org/abs/2310.02019v1
• Date: Tue, 3 Oct 2023 12:48:57 GMT
• Title: Towards Feasible Counterfactual Explanations: A Taxonomy Guided Template-based NLG Method
• Authors: Pedram Salimi, Nirmalie Wiratunga, David Corsar, Anjana Wijekoon
• Abstract summary: Counterfactual Explanations (cf-XAI) describe the smallest changes in feature values necessary to change an outcome from one class to another. Many cf-XAI methods neglect the feasibility of those changes. We introduce a novel approach for presenting cf-XAI in natural language (Natural-XAI)
• Score: 0.5003525838309206
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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