Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces

• URL: http://arxiv.org/abs/2110.00762v1
• Date: Sat, 2 Oct 2021 09:06:36 GMT
• Title: Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces
• Authors: Francesco Sovrano, Fabio Vitali
• Abstract summary: We show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms. Our contribution is an approach to frame illocution in a computer-friendly way, to achieve user-centrality with statistical question answering. We tested our hypotheses with a user-study involving more than 60 participants, on two XAI-based systems.
• Score: 3.04585143845864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new method for generating explanations with 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. 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. In fact, we frame illocution, in 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. More precisely, we hypothesise that given an arbitrary explanatory process, increasing its goal-orientedness and degree of illocution results in the generation of more usable (as per ISO 9241-210) explanations. We tested our hypotheses with a user-study involving more than 60 participants, on two XAI-based systems, one for credit approval (finance) and one for heart disease prediction (healthcare). The results showed that our proposed solution produced a statistically significant improvement (hence with a p-value lower than 0.05) 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.

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