Over the past decade explainable artificial intelligence has evolved from a predominantly technical discipline into a field that is deeply intertwined with social sciences. Insights such as human preference for contrastive -- more precisely, counterfactual -- explanations have played a major role in this transition, inspiring and guiding the research in computer science. Other observations, while equally important, have received much less attention. The desire of human explainees to communicate with artificial intelligence explainers through a dialogue-like interaction has been mostly neglected by the community. This poses many challenges for the effectiveness and widespread adoption of such technologies as delivering a single explanation optimised according to some predefined objectives may fail to engender understanding in its recipients and satisfy their unique needs given the diversity of human knowledge and intention. Using insights elaborated by Niklas Luhmann and, more recently, Elena Esposito we apply social systems theory to highlight challenges in explainable artificial intelligence and offer a path forward, striving to reinvigorate the technical research in this direction. This paper aims to demonstrate the potential of systems theoretical approaches to communication in understanding problems and limitations of explainable artificial intelligence.

翻译：过去十年间，可解释人工智能已从一门以技术为主导的学科发展为与社会学深度交织的领域。人类偏好对比性（更确切地说是反事实）解释等洞见在此转变中发挥了重要作用，启发并指导了计算机科学领域的研究。然而，其他同等重要的观察结果却鲜少受到关注。人类解释对象希望通过类似对话的互动与人工智能解释器进行沟通的需求，在很大程度上被学界所忽视。这为相关技术的有效性及广泛采用带来了诸多挑战——根据预设目标优化生成单一解释，可能无法在接收者心中建立理解，也无法满足人类知识与意图多样性背景下的独特需求。借鉴尼克拉斯·卢曼及近期埃琳娜·埃斯波西托的研究成果，我们运用社会系统理论揭示可解释人工智能面临的挑战，并指明前进方向，力求重振该领域的技术研究。本文旨在证明系统理论方法在理解可解释人工智能问题与局限性方面的潜力。