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

• URL: http://arxiv.org/abs/2302.03460v3
• Date: Thu, 11 Jul 2024 12:13:04 GMT
• Title: Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication
• Authors: Bernard Keenan, Kacper Sokol,
• Abstract summary: We apply social systems theory to highlight challenges in explainable artificial intelligence. We aim to reinvigorate the technical research in the direction of interactive and iterative explainers.
• Score: 5.742215677251865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain [0.7770029179741429]
  The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
  arXiv  Detail & Related papers  (2024-02-07T14:09:11Z)
• Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
  We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
  arXiv  Detail & Related papers  (2023-11-13T21:20:17Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence [11.472707084860875]
  We define explainability as (logical) reasoning applied to transparent insights (into black boxes) interpreted under certain background knowledge. We revisit the trade-off between transparency and predictive power and its implications for ante-hoc and post-hoc explainers. We discuss components of the machine learning workflow that may be in need of interpretability, building on a range of ideas from human-centred explainability.
  arXiv  Detail & Related papers  (2021-12-29T09:21:33Z)
• Projection: A Mechanism for Human-like Reasoning in Artificial Intelligence [6.218613353519724]
  Methods of inference exploiting top-down information (from a model) have been shown to be effective for recognising entities in difficult conditions. Projection is shown to be a key mechanism to solve the problem of applying knowledge to varied or challenging situations.
  arXiv  Detail & Related papers  (2021-03-24T22:33:51Z)
• Machine Common Sense [77.34726150561087]
  Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI) This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
  arXiv  Detail & Related papers  (2020-06-15T13:59:47Z)
• Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
  We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
  arXiv  Detail & Related papers  (2020-03-09T15:04:07Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)
• Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach [18.14698948294366]
  We introduce Human-centered Explainable AI (HCXAI) as an approach that puts the human at the center of technology design. It develops a holistic understanding of "who" the human is by considering the interplay of values, interpersonal dynamics, and the socially situated nature of AI systems.
  arXiv  Detail & Related papers  (2020-02-04T02:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.