  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.
