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. 05178v1 Announce Type: cross Abstract: As AI-driven product development accelerates, the bottleneck is shifting from how we build to what we build.
By Tim Dorn, Saara A. Khan, Julie Mumford
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:2609.14699v1 Announce Type: new
Abstract: Visual content shapes audience perception and opinion on social media, and computational social science increasingly relies on automated tools to analy...
By Weihong Qi, Chen Ling
AI systems can strengthen democracy by supporting deliberation at scale by addressing cognitive, social, platform-design, and market-driven frictions, while preserving human agency. Unlike proposals s...
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:2601. 11072v1 Announce Type: cross Abstract: Within journalistic editorial processes, disclosing AI usage is currently limited to simplistic labels, which misses the nuance of how humans and AI collaborated on a news article.
By Amber Kusters, Pooja Prajod, Pablo Cesar, Abdallah El Ali
The paper investigates whether large language model (LLM) agents can simulate public deliberation by reflecting population opinion patterns and producing interaction-driven opinion change. Using census‑grounded Korean personas debating real policy questions, the study finds that persona agents fail to reliably reproduce population opinion patterns, often concentrating responses and reversing demographic differences. While deliberations generate reasoned, reciprocal arguments and some stance movement, much of this change occurs without peer exchange, and anchoring agents to population‑informed starting positions suppresses updating, indicating that population representation, argument generation, and interaction‑driven opinion change do not necessarily align.
By Chaemin Jang, Junsik Min, Jaewoo Choi, Donggyu Lee, Haiin Lee, Junyoung Park, Namhee Kim, Hyunwoo Kim, Jungwon Kim, Juho Kim, Nuri Kim, Jihee Kim
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
The paper investigates how large language models (LLMs) can perform multi-coder qualitative coding by independently coding, debating, and reconciling disagreements. It quantifies the effectiveness of this approach across diverse datasets, identifying key factors—such as codebook length, data similarity, and agent disagreement—that influence coding accuracy. The study finds that intense, unresolved debates improve accuracy but that LLMs still lack adaptive responsiveness to context, leading to design recommendations for automated coding systems.
By Jeongyeon Kim, John Mitchell
arXiv:2606. 00370v1 Announce Type: cross Abstract: Diverse genomics data, scientific questions, and analysis tasks typically demand highly specialized visualizations.
By Astrid van den Brandt, Kiroong Choe, Sehi L'Yi, Devin Lange, Nils Gehlenborg
arXiv:2607. 25911v1 Announce Type: cross Abstract: Annotation is among the most demanding visualization tasks to automate, as it simultaneously requires correctly navigating visual, semantic, and stylistic constraints.
By Md Rahat-uz-Zaman, Md Dilshadur Rahman, Andrew McNutt, Paul Rosen