Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication

TL;DR

This paper reframes explainable AI as a problem of social communication rather than purely technical information transfer, drawing on Niklas Luhmann’s social systems theory and Elena Esposito’s work to address the sociotechnical gap. It argues that effective XAI requires interactive, iterative explanations and recognition of second-order observation, structural couplings, and functional differentiation across domains such as law, medicine, and economics. By applying a tripartite information–utterance–understanding model, the authors show how explainer designs must function as second-order observers that tailor information and media to the explainee’s situated context and social environment. The healthcare case study illustrates the practical challenges of integrating ADM tools into complex, high-stakes workflows, underscoring the need for organizational accountability and human-centered reasoning alongside technical explanations. Overall, the work advocates for treating XAI as artificial communication embedded within evolving social systems, with implications for design, evaluation, and governance of explainable AI in real-world settings.

Abstract

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.