  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.
