# Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication arXiv: https://arxiv.org/abs/2302.03460 ยท HTML: https://mlsanity.com/paper/2302.03460/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 101 references that are in the map. - First posted on arXiv 7 Feb 2023. ## Builds on (most cited first, 25 of 25) - [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017) - [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017) - [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017) - [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018) - [Algorithmic Recourse: from Counterfactual Explanations to Interventions](https://mlsanity.com/paper/2002.06278/) (2020) - [FACE: Feasible and Actionable Counterfactual Explanations](https://mlsanity.com/paper/1909.09369/) (2019) - [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019) - [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019) - [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017) - [One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency](https://mlsanity.com/paper/2001.09734/) (2020) - [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://mlsanity.com/paper/1902.01876/) (2019) - [Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts](https://mlsanity.com/paper/2201.10295/) (2022) - [The Flaws of Policies Requiring Human Oversight of Government Algorithms](https://mlsanity.com/paper/2109.05067/) (2021) - [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023) - [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023) - [Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems](https://mlsanity.com/paper/2102.04201/) (2021) - [A Grounded Interaction Protocol for Explainable Artificial Intelligence](https://mlsanity.com/paper/1903.02409/) (2019) - [Levels of explainable artificial intelligence for human-aligned conversational explanations](https://mlsanity.com/paper/2107.03178/) (2021) - ["There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making](https://mlsanity.com/paper/2205.05758/) (2022) - [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021) - [What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations](https://mlsanity.com/paper/2002.05674/) (2020) - [Interpretable Representations in Explainable AI: From Theory to Practice](https://mlsanity.com/paper/2008.07007/) (2020) - [Explanation as a process: user-centric construction of multi-level and multi-modal explanations](https://mlsanity.com/paper/2110.03759/) (2021) - [The Role of Normware in Trustworthy and Explainable AI](https://mlsanity.com/paper/1812.02471/) (2018) - [What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components](https://mlsanity.com/paper/2209.03813/) (2022) ## Built on by (most cited first, 9 of 9) - [Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap](https://mlsanity.com/paper/2506.05030/) (2025) - [(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility](https://mlsanity.com/paper/2306.02312/) (2023) - [What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks](https://mlsanity.com/paper/2403.12730/) (2024) - [Navigating Explanatory Multiverse Through Counterfactual Path Geometry](https://mlsanity.com/paper/2306.02786/) (2023) - [Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations](https://mlsanity.com/paper/2303.00934/) (2023) - [From Logic Monopoly to Social Contract: Separation of Power and the Institutional Foundations for Autonomous Agent Economies](https://mlsanity.com/paper/2603.25100/) (2026) - [Suboptimal Shapley Value Explanations](https://mlsanity.com/paper/2502.12209/) (2025) - [From Attribution to Action: A Human-Centered Application of Activation Steering](https://mlsanity.com/paper/2604.11467/) (2026) - [Asymmetric Communication: Large Language Models and Language Games](https://mlsanity.com/paper/2607.28137/) (2026) ## Abstract (arXiv) 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.