  We propose a new method for generating explanations with 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. With this work we aim to
prove that the philosophical theory of explanations presented by Achinstein can
be actually adapted for being implemented into a concrete software application,
as an interactive and illocutionary process of answering questions.
Specifically, our contribution is an approach to frame illocution in a
computer-friendly way, to achieve user-centrality with statistical question
answering. Indeed, we frame the illocution of an explanatory process as that
mechanism responsible for anticipating the needs of the explainee in the form
of unposed, implicit, archetypal questions, hence improving the user-centrality
of the underlying explanatory process. Therefore, we hypothesise that if an
explanatory process is an illocutionary act of providing content-giving answers
to questions, and illocution is as we defined it, the more explicit and
implicit questions can be answered by an explanatory tool, the more usable its
explanations. We tested our hypothesis with a user-study involving more than 60
participants, on two XAI-based systems, one for credit approval and one for
heart disease prediction. The results showed that increasing the illocutionary
power of an explanatory tool can produce statistically significant improvements
on effectiveness. This, combined with a visible alignment between the
increments in effectiveness and satisfaction, suggests that our understanding
of illocution can be correct, giving evidence in favour of our theory.
