The paper argues that AI can strengthen democracy by supporting large‑scale deliberation, addressing cognitive, social, platform‑design, and market frictions while preserving human agency. It contrasts AI‑assisted deliberation with liquid democracy, claiming the former lowers barriers to meaningful engagement without replacing human judgment. The authors outline four guiding principles—preserving agency, encouraging mutual respect, promoting equality, and augmenting active citizenship—and discuss challenges such as alignment, sycophancy, bias, and over‑reliance. They call on the machine learning community to develop and evaluate deliberation‑focused AI systems based on their ability to facilitate informed, representative, and friction‑robust discourse.
By Jos\'e Ram\'on Enr\'iquez, Jiaxin Pei, Alex Pentland
arXiv:2606. 13026v1 Announce Type: cross Abstract: Interfacing Artificial Intelligence (AI) with democracy is one of the most profound challenges of our times.
By Evangelos Pournaras, Srijoni Majumdar, Carina Hausladen, Dirk Helbing
The paper proposes new conditions for philosophers to engage with citizen deliberation in the AI era, focusing on how Large Language Models (LLMs) could support democratic processes such as citizen assemblies. It outlines the Democratic Commons project, an interdisciplinary effort that evaluates LLMs against five democratic principles, with a central concern about political bias and the democratic use of AI in experimental participatory settings. The study emphasizes the need for philosophical and political theory foundations to meaningfully assess AI’s role in democratic participation.
By Bernard Reber (CEVIPOF)
The paper examines how Preference Inference (PI) models used in large-scale participatory democracy platforms can alter the perceived consensus and minority support by predicting missing votes. It introduces a collective‑centric evaluation framework that assesses whether inferred votes maintain key properties of the overall preference landscape, rather than focusing solely on individual prediction accuracy. Using the largest multilingual dataset to date—four consultations with over 90,000 participants, 1 million votes, and 22 languages—the study finds that models with similar predictive accuracy can differ markedly in how well they preserve the collective structure, underscoring that accuracy alone is insufficient for evaluating PI in democratic contexts.
By Pierre-Antoine Lequeu, Salim Hafid, Paul Lerner, Nazanin Shafiabadi, Laur\`ene Cave, David Mas, Jean-Philippe Cointet, Benjamin Piwowarski, Fran\c{c}ois Yvon
arXiv:2606. 28294v1 Announce Type: new Abstract: Preference-based alignment often struggles to capture the reasoning that underlies human judgments.
By Kevin Kingslin, Anish Natekar, Ashutosh Ranjan, Vivek Srivastava, Savita Bhat, Shirish Karande
arXiv:2608. 10186v1 Announce Type: cross Abstract: LLMs are increasingly deployed in settings that require collective reasoning on complex, value-laden problems.
By Maurice Flechtner
The paper examines whether AI‑driven debate can help humans and weaker language models (LLMs) correct their judgments. Experiments show that debate improves human accuracy over prior beliefs, especially with weighted voting, but offers little benefit—and sometimes harms—accuracy for weaker LLM supervisors. The study highlights debate as a promising tool for scalable human oversight while questioning its effectiveness for fully automated AI control.
By Maria Victoria Carro, Denise Alejandra Mester, Facundo Nieto, Oscar Agustin Stanchi, Guido Ernesto Bergman, Giovanni Franco Gabriel Marrafini, Trinidad Borrell, Guido Freire, Joaquin Salvador Machulsky, Mario Alejandro Leiva, Ana Lopez, Nicolas Spinelli, Luca Nicolas Forziati Gangi, Eitan Sprejer, Federico Barrera Lemarchand, Gerardo Simari, Maria Vanina Martinez
The paper examines how large language models (LLMs) emulate voters in digital democracy and the demographic biases that arise. By analyzing three U.S. presidential elections, the authors compare a closed‑source and an open‑source LLM, finding that removing gender or education from voter profiles can significantly reduce group errors, while removing race increases errors. Common alignment tactics such as repeated queries or fairness instructions fail to mitigate these disparities and can even worsen them.
By Yi Qi, Srijoni Majumdar, Evangelos Pournaras
arXiv:2608.30842v1 Announce Type: new
Abstract: Humans play a vital role at every stage of AI development, from data collection and curation to model development and evaluation. However, humans often...
By Deepak Pandita, Christopher M. Homan
arXiv:2608. 03910v1 Announce Type: new Abstract: As AI systems are deployed across increasingly diverse social contexts, alignment can no longer be framed as the optimization of a single, unified set of values.
By Matt Ratto, Abhishek Moturu, Daniel Silver
arXiv:2606. 24635v1 Announce Type: cross Abstract: Traditional visual data storytelling relies on binary graphics that depict two simplified groups in conflict.
By Lisa Schirch, Beth Goldberg
OmouAI is an interactive deliberation system that combines large language models with computational argumentation to facilitate policy debates involving humans and simulated personas such as stakeholders, experts, or devil’s advocates. Each persona generates its own arguments, which are assembled into a shared argumentation framework that users can contest, add to, or revise, ensuring human oversight. The system evaluates arguments using deterministic argumentative semantics against external goals like the UN Sustainable Development Goals, providing faithful explanations and indicating how policy recommendations affect those goals.
By Stylianos Loukas Vasileiou, Antonio Rago, William Yeoh, Georgina Curto