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We propose a new method for explanations in Artificial Intelligence (AI) anda tool to test its expressive power within a user interface. In order to bridgethe gap between philosophy and human-computer interfaces, we show a newapproach for the generation of interactive explanations based on asophisticated pipeline of AI algorithms for structuring natural languagedocuments into knowledge graphs, answering questions effectively andsatisfactorily. Among the mainstream philosophical theories of explanation weidentified one that in our view is more easily applicable as a practical modelfor user-centric tools: Achinstein's Theory of Explanation. With this work weaim to prove that the theory proposed by Achinstein can be actually adapted forbeing implemented into a concrete software application, as an interactiveprocess answering questions. To this end we found a way to handle the generic(archetypal) questions that implicitly characterise an explanatory processes aspreliminary overviews rather than as answers to explicit questions, as commonlyunderstood. To show the expressive power of this approach we designed andimplemented a pipeline of AI algorithms for the generation of interactiveexplanations under the form of overviews, focusing on this aspect ofexplanations rather than on existing interfaces and presentation logic layersfor question answering. We tested our hypothesis on a well-known XAI-poweredcredit approval system by IBM, comparing CEM, a static explanatory tool forpost-hoc explanations, with an extension we developed adding interactiveexplanations based on our model. The results of the user study, involving morethan 100 participants, showed that our proposed solution produced astatistically relevant improvement on effectiveness (U=931.0, p=0.036) over thebaseline, thus giving evidence in favour of our theory.

From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein's Theory of Explanation