关注差距！用卢曼的传播功能理论弥合可解释人工智能与人类理解之间的鸿沟
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication
- School of Law, Birkbeck, University of London（伦敦大学伯贝克学院法学院）
- Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）
- ARC Centre of Excellence for Automated Decision-Making and Society, RMIT University（皇家墨尔本理工大学自动化决策与社会ARC卓越研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对可解释人工智能领域忽视人类类对话交互解释需求的问题，本文运用卢曼等的社会系统理论揭示相关挑战，提出发展交互式、迭代式解释器的方向，论证了系统传播理论的应用潜力。
AI中文摘要：
在过去十年间，可解释人工智能（explainable artificial intelligence，XAI）已从一门以技术为主的学科发展为与社会科学深度交织的领域。诸如人类偏好对比性解释——更准确地说是反事实解释——这类洞见在这一转变中发挥了重要作用，启发并指导着计算机科学领域的研究。而其他同样重要的观察结果却得到的关注少得多。人类被解释者希望通过类对话交互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。这给此类技术的有效性和广泛普及带来了诸多挑战，因为鉴于人类知识与意图的多样性，仅提供根据某些预定义目标优化的单一解释，可能无法让接收者产生理解，也无法满足他们的独特需求。本文借助尼克拉斯·卢曼（Niklas Luhmann）以及近年埃琳娜·埃斯波西托（Elena Esposito）阐述的洞见，运用社会系统理论揭示可解释人工智能领域的挑战，并提出一条前进路径，力求推动技术研究朝着交互式、迭代式解释器的方向发展。具体而言，本文论证了传播的系统理论方法在阐明和解决以人为中心的可解释人工智能的问题与局限性方面的潜力。
英文摘要：
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 nevertheless received much less consideration. 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 the direction of interactive and iterative explainers. Specifically, this paper demonstrates the potential of systems theoretical approaches to communication in elucidating and addressing the problems and limitations of human-centred explainable artificial intelligence.
