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

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Francesco Sovrano, Fabio Vitali
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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. 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 (as per ISO 9241-210) its explanations. We tested our hypothesis 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 increasing the illocutionary power of an explanatory tool can produce statistically significant improvements (hence with a P value lower than .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.

通过语用问答生成以用户为中心的解释:从哲学到界面
我们提出了一种用人工智能(AI)生成解释的新方法,以及一种在用户界面中测试其表达能力的工具。为了弥合哲学和人机界面之间的差距,我们展示了一种基于复杂的人工智能算法管道生成交互式解释的新方法,用于将自然语言文档构建为知识图,有效且令人满意地回答问题。通过这项工作,我们的目标是证明阿奇斯坦提出的哲学解释理论实际上可以作为回答问题的交互式和言外之语过程应用于具体的软件应用程序中。具体来说,我们的贡献是以一种计算机友好的方式来构建illoction,以实现以统计问题回答为中心的用户。事实上,我们将解释过程的言外之意定义为一种机制,负责以未提出的、隐含的、原型问题的形式预测被解释者的需求,从而提高潜在解释过程的用户中心性。因此,我们假设,如果解释过程是一种提供内容给出问题答案的言外行为,而言外行为正如我们所定义的那样,那么解释工具可以回答的显性和隐性问题越多,其解释就越有用(根据ISO 9241-210)。我们用两个基于xai的系统,一个用于信贷审批(金融),一个用于心脏病预测(医疗),对60多名参与者进行了用户研究,以检验我们的假设。结果表明,增加解释工具的言外能力可以在有效性上产生统计学上显著的改善(因此P值低于0.05)。这与有效性和满意度之间的明显一致性相结合,表明我们对非言语的理解可能是正确的,为我们的理论提供了证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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