  We propose a new method for explanations in Artificial Intelligence (AI) and
a tool to test its expressive power within a user interface. In order to bridge
the gap between philosophy and human-computer interfaces, we show a new
approach for the generation of interactive explanations based on a
sophisticated pipeline of AI algorithms for structuring natural language
documents into knowledge graphs, answering questions effectively and
satisfactorily. Among the mainstream philosophical theories of explanation we
identified one that in our view is more easily applicable as a practical model
for user-centric tools: Achinstein's Theory of Explanation. With this work we
aim to prove that the theory proposed by Achinstein can be actually adapted for
being implemented into a concrete software application, as an interactive
process answering questions. To this end we found a way to handle the generic
(archetypal) questions that implicitly characterise an explanatory processes as
preliminary overviews rather than as answers to explicit questions, as commonly
understood. To show the expressive power of this approach we designed and
implemented a pipeline of AI algorithms for the generation of interactive
explanations under the form of overviews, focusing on this aspect of
explanations rather than on existing interfaces and presentation logic layers
for question answering. We tested our hypothesis on a well-known XAI-powered
credit approval system by IBM, comparing CEM, a static explanatory tool for
post-hoc explanations, with an extension we developed adding interactive
explanations based on our model. The results of the user study, involving more
than 100 participants, showed that our proposed solution produced a
statistically relevant improvement on effectiveness (U=931.0, p=0.036) over the
baseline, thus giving evidence in favour of our theory.
