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主要的技术学科演变为与社会深度交叉的领域。人类偏好对比性——更精确地说，反事实——解释等见解在这一转变中发挥了重要作用，启发并指导了计算机科学研究。其他同样重要的观察却较少受到关注。人类解释者渴望通过类似对话的互动与人工智能解释者进行沟通，这一需求在很大程度上被学界忽视。这给此类技术的有效性和广泛采用带来了诸多挑战：鉴于人类知识与意图的多样性，仅根据预设目标优化单一解释可能无法让接收者产生理解，也无法满足其独特需求。借鉴尼古拉斯·卢曼及近期埃琳娜·埃斯波西托阐述的见解，我们运用社会系统理论来凸显可解释人工智能面临的挑战，并指明前进方向，力图重振这方面的技术研究。本文旨在展示系统理论方法在理解可解释人工智能的问题与局限性方面的潜力。