Human/AI Collective Intelligence for Deliberative Democracy: A Human-Centred Design Approach

• URL: http://arxiv.org/abs/2603.16260v1
• Date: Tue, 17 Mar 2026 08:47:54 GMT
• Title: Human/AI Collective Intelligence for Deliberative Democracy: A Human-Centred Design Approach
• Abstract summary: This chapter introduces the concept of Collective Intelligence for Deliberative Democracy (CI4DD)<n>We propose that the use of computational tools, specifically artificial intelligence, is an instantiation of a broader class of human-computer system designed to augment collective intelligence.
• Score: 3.4857788301757764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This chapter introduces the concept of Collective Intelligence for Deliberative Democracy (CI4DD). We propose that the use of computational tools, specifically artificial intelligence to advance deliberative democracy, is an instantiation of a broader class of human-computer system designed to augment collective intelligence. Further, we argue for a fundamentally human-centred design approach to orchestrate how stakeholders can contribute meaningfully to shaping the artifacts and processes needed to create trustworthy DD processes. We first contextualise the key concepts of CI and the role of AI within it. We then detail our co-design methodology for identifying key challenges, refining user scenarios, and deriving technical implications. Two exemplar cases illustrate how user requirements from civic organisations were implemented with AI support and piloted in authentic contexts.

Related papers

• Habermolt: Delegating Deliberation to AI Representatives [4.882694170903341]
  Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth.<n>Recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, but remain time-intensive for individual users.<n>As AI models become increasingly capable, AI systems are being deployed not only to mediate deliberation between humans, but to represent humans in it.
  arXiv  Detail & Related papers  (2026-05-23T05:50:50Z)
• AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence [15.888253013073632]
  We argue that collective input can and should be incorporated at multiple points across the ML development pipeline.<n>Social choice provides a well-suited modelling language for the treatment of collective input across all stages.<n>Our conceptual contribution provides a mathematically grounded framework to implement and analyse collective control of AI systems.
  arXiv  Detail & Related papers  (2026-04-14T07:42:19Z)
• The Workflow as Medium: A Framework for Navigating Human-AI Co-Creation [0.0]
  The Creative Intelligence Loop (CIL) is a novel socio-technical framework for responsible human-AI co-creation.<n>The CIL proposes a disciplined structure for dynamic human-AI collaboration, guiding the strategic integration of diverse AI teammates.<n>The CIL was empirically demonstrated through the practice-led creation of two graphic novellas.
  arXiv  Detail & Related papers  (2025-11-22T20:36:13Z)
• From Explainable to Explanatory Artificial Intelligence: Toward a New Paradigm for Human-Centered Explanations through Generative AI [0.0]
  "Explanatory AI" is a paradigm that leverages generative AI capabilities to serve as explanatory partners for human understanding.<n>We develop a conceptual model distinguishing Explanatory AI through narrative communication, adaptive personalization, and progressive disclosure principles.<n>Our findings reveal the practical urgency for AI systems designed for human comprehension rather than algorithmic introspection.
  arXiv  Detail & Related papers  (2025-08-08T14:32:41Z)
• The Right to AI [3.2132738637761027]
  This paper proposes a Right to AI, which asserts that individuals and communities should meaningfully participate in the development and governance of the AI systems that shape their lives.<n>We critically evaluate how generative agents, large-scale data extraction, and diverse cultural values bring new complexities to AI oversight.
  arXiv  Detail & Related papers  (2025-01-29T04:32:41Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• 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)
• PADTHAI-MM: Principles-based Approach for Designing Trustworthy, Human-centered AI using MAST Methodology [5.215782336985273]
  The Multisource AI Scorecard Table (MAST) was designed to bridge the gap by offering a systematic, tradecraft-centered approach to evaluating AI-enabled decision support systems.<n>We introduce an iterative design framework called textitPrinciples-based Approach for Designing Trustworthy, Human-centered AI.<n>We demonstrate this framework in our development of the Reporting Assistant for Defense and Intelligence Tasks (READIT)
  arXiv  Detail & Related papers  (2024-01-24T23:15:44Z)
• On some Foundational Aspects of Human-Centered Artificial Intelligence [52.03866242565846]
  There is no clear definition of what is meant by Human Centered Artificial Intelligence. This paper introduces the term HCAI agent to refer to any physical or software computational agent equipped with AI components. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI.
  arXiv  Detail & Related papers  (2021-12-29T09:58:59Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)
• 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)

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.