State of the art Artificial Intelligence (AI) techniques have reached an impressive complexity. Consequently, researchers are discovering more and more methods to use them in real-world applications. However, the complexity of such systems requires the introduction of methods that make those transparent to the human user. The AI community is trying to overcome the problem by introducing the Explainable AI (XAI) field, which is tentative to make AI algorithms less opaque. However, in recent years, it became clearer that XAI is much more than a computer science problem: since it is about communication, XAI is also a Human-Agent Interaction problem. Moreover, AI came out of the laboratories to be used in real life. This implies the need for XAI solutions tailored to non-expert users. Hence, we propose a user-centred framework for XAI that focuses on its social-interactive aspect taking inspiration from cognitive and social sciences' theories and findings. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
翻译：人工智能(AI)技术已经达到令人印象深刻的复杂程度,因此,研究人员正在发现越来越多的方法在现实世界应用中使用这些技术,然而,由于这些系统的复杂性,需要采用使这些方法对人类用户具有透明度的方法。AI社区正试图通过引入可解释的AI(XAI)领域来克服问题,该领域是使人工智能算法不那么不透明的试探性,但近年来,日益明显的是,XAI远不止是一个计算机科学问题:由于它涉及通信,XAI也是一个人际互动问题。此外,AI是从实验室中出来的,用于现实生活中。这意味着需要为非专家用户量身定制XAI解决方案。因此,我们提出一个以用户为中心的 XAI框架,侧重于其社会互动方面,从认知和社会科学理论及发现中得到启发。该框架旨在为非专家用户提供交互式XAI解决方案的结构。
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