Deliberative Democracy

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Deliberative democracy studies how people with differing views can exchange reasons, learn from one another, and contribute to legitimate collective decisions. Work in the field develops methods for selecting representative participants and arguments, and uses opinion maps, AI facilitators, and simulated personas to support discussion at larger scales. Researchers assess these systems through measures such as representation, perspective diversity, procedural quality, trust, and whether participants can inspect or revise how their views are presented. A central tension is whether AI can broaden participation without narrowing viewpoints, obscuring its influence, or substituting machine judgment for human agency.

Latest papers

16 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Large Language Models and Augmented Democracy
Oct 3, 2026Jairo Gudiño-RoseroDigital Twins Large Language Model-Based Social Simulation
Doctoral School of Mathematics, Computer Science and Telecommunications of Toulouse, Toulouse, France
OmouAI: Argumentative Human-AI Policy Deliberation with Simulated Personas
Sep 25, 2026Stylianos Loukas Vasileiou, Antonio Rago, William Yeoh +1Multi-Agent Deliberation Human-in-the-Loop AI
New Mexico State University · King’s College London · Washington University in St. Louis +1
AI Should Facilitate Democratic Deliberation at Scale
Sep 17, 2026José Ramón Enríquez, Jiaxin Pei, Alex PentlandHuman-Centered AI Deliberative Democracy
Stanford Graduate School of Business, Stanford, CA, USA · Stanford Institute for Human-Centered AI, Stanford, CA, USA · MIT Media Lab, Cambridge, MA, USA
Rules Before Oracles: Auditable, User-Configurable Argument Selection for Deliberative Polling
Aug 25, 2026Muntaser Syed, Markus Zanker, Marius SilaghiDeliberative Democracy Recommender Systems
Florida Institute of Technology, Melbourne, Florida, USA · Free University of Bozen-Bolzano, Bolsano, Italy
The Deliberative Deficit: An Empirical Critique of LLMs in Democratic Discourse
Aug 10, 2026Maurice FlechtnerLLM Evaluation Multi-Agent Debate
University of Zurich, Zurich, Switzerland · ETH Zurich, Zurich, Switzerland
Visualizing "We the People": Bridging the Perception Gap through Pluralistic Data Storytelling
Jun 23, 2026Lisa Schirch, Beth GoldbergDeliberative Democracy
University of Notre Dame · Jigsaw
The Almost Intelligent Revolution: Options for Scaling Up Deliberation and Empowering People with AI
Jun 18, 2026Serge SharoffSocial Bias in Language Models Computational Argumentation
School of Languages, Cultures and Societies, University of Leeds, UK
Democracy in the Era of Artificial Intelligence
Jun 11, 2026Evangelos Pournaras, Srijoni Majumdar, Carina Hausladen +1Deliberative Democracy AI Governance
School of Computer Science, University of Leeds, Leeds, UK. · School of Energy Systems, LUT University, Finland · Computational Social Science, University of Konstanz, Konstanz, Germany. +2
Accountable Human-AI Deliberation with LLMs: Scaling Collective Intelligence through Symbiotic Scaffolding
May 26, 2026Wajdi ZaghouaniAI Accountability Human-in-the-Loop AI
Northwestern University in Qatar
Habermolt: Delegating Deliberation to AI Representatives
May 23, 2026Joseph Low, Oscar Duys, Claude Formanek +2Deliberative Democracy AI Agent Governance
Cooperative AI Research Fellowship · AI Safety South Africa · MIT +1
Epistemic orientation in parliamentary discourse is associated with deliberative democracy
Apr 21, 2026Segun Aroyehun, Stephan Lewandowsky, David GarciaEvidence-Grounded Reasoning Political Discourse Analysis
Department of Politics and Public Administration, University of Konstanz, Germany. · School of Psychological Science, University of Bristol, UK. · Department of Psychology, University of Potsdam, Germany. +1
AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent
Apr 7, 2026Suyash Fulay, Prerna Ravi, Emily Kubin +3AI-Assisted Decision Making Human-AI Decision Making
Massachusetts Institute of Technology Cambridge, Massachusetts, USA · Oxford University Oxford, United Kingdom
Human/AI Collective Intelligence for Deliberative Democracy: A Human-Centred Design Approach
Mar 17, 2026Anna De Liddo, Lucas Anastasiou, Simon Buckingham ShumDeliberative Democracy Human-AI Co-Creation
Knowledge Media Institute, The Open University, Milton Keynes, UK · Connected Intelligence Centre, University of Technology Sydney, Sydney, Australia
Agora: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice
Mar 7, 2026Prerna Ravi, Om Gokhale, Suyash Fulay +3Educational Technology Deliberative Democracy
Massachusetts Institute of Technology · University of Oxford
Temporal Panel Selection in Ongoing Citizens' Assemblies
Feb 18, 2026Yusuf Hakan Kalayci, Evi MichaSocial Choice Theory Algorithmic Fairness
University of Southern California
Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study
Feb 17, 2026Salim Hafid, Manon Berriche, Jean-Philippe CointetDiversity-Aware Sampling Data Selection
m´edialab, Sciences Po Paris, France.

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Large Language Models and Augmented Democracy

Jairo Gudiño-Rosero

Doctoral School of Mathematics, Computer Science and Telecommunications of Toulouse, Toulouse, France

Digital Twins Large Language Model-Based Social Simulation Deliberative Democracy Prompt Injection Attacks on LLMs

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Artificial intelligence enables computational agents to represent political preferences and take part in collective decision-making. In this thesis, I investigate the opportunities and challenges of digital twins (DTs) based on Large Language Models (LLMs) as intermediaries in augmented democracy, focusing on individual preference representation, collective representation of political organizations, and the vulnerability of those representations to attackers. First, using data from an online experiment in Brazil, I examine whether personalized DTs can predict citizens' preferences for unseen policy proposals. Second, I extend the DT framework from individuals to political organizations. Using Swiss parliamentary data, I build topic-specific knowledge graphs from lawmakers' legislative records and connect them to LLM-based lawmaker agents, which are organized into party-level DTs representing collective positions. Agentic deliberation among these agents tests whether aggregated party representations capture a broader range of intra-party perspectives than official party communications. Finally, I study the vulnerability and robustness of LLM-mediated deliberation against prompt-injection attacks that amplify viewpoints, suppress opinions, or redirect consensus. Using data from a 2023 deliberative experiment in the United Kingdom, I analyze how attack effectiveness varies with the distribution of opinions and rhetorical strategies, and evaluate a pipeline combining injection detection, structured opinion representations, and reinforcement learning to improve resistance. These findings characterize the opportunities and challenges of LLM-based digital twins in augmented democracy, stressing accurate preference representation, faithful aggregation, and robustness to strategic interaction.