Hugging Face Trending Papers
AI Should Facilitate Democratic Deliberation at Scale
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arXiv AI
5d ago
AI Should Facilitate Democratic Deliberation at Scale
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.
arXiv AI
Jun 12
Democracy in the Era of Artificial Intelligence
arXiv:2606. 13026v1 Announce Type: cross Abstract: Interfacing Artificial Intelligence (AI) with democracy is one of the most profound challenges of our times.
arXiv AI
Sep 15
New Conditions for Philosophers to Catch the Wave of Citizen Deliberation in the Age of Artificial Intelligence in advance
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.
arXiv AI
Sep 4
Toward Collective-Centric Evaluation of Preference Inference for Participatory Democracy
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.
arXiv Machine Learning
Jun 29
Democratic ICAI: Debating Our Way to Steering Principles from Preferences
arXiv:2606. 28294v1 Announce Type: new Abstract: Preference-based alignment often struggles to capture the reasoning that underlies human judgments.
arXiv AI
Aug 12
The Deliberative Deficit: An Empirical Critique of LLMs in Democratic Discourse
arXiv:2608. 10186v1 Announce Type: cross Abstract: LLMs are increasingly deployed in settings that require collective reasoning on complex, value-laden problems.