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.
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:2606. 13026v1 Announce Type: cross Abstract: Interfacing Artificial Intelligence (AI) with democracy is one of the most profound challenges of our times.
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.
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:2608. 10186v1 Announce Type: cross Abstract: LLMs are increasingly deployed in settings that require collective reasoning on complex, value-laden problems.
arXiv:2509.24877v3 Announce Type: replace Abstract: The social science of large language models (LLMs) examines how these systems evoke mind attributions, interact with one another, and transform hum...
arXiv:2606. 12437v1 Announce Type: cross Abstract: The increasing encroachment of artificial intelligence (AI) on social life raises significant risks for society, particularly within the infospheres created and controlled by companies such as Google, Facebook, Apple, and Amazon.
arXiv:2606. 13755v1 Announce Type: cross Abstract: We argue that aligning AI to aggregated human preferences is the wrong target.
arXiv:2606. 01929v1 Announce Type: new Abstract: Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public.
How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of...
arXiv:2609.23039v1 Announce Type: new Abstract: LLM-based AI systems answer political questions for hundreds of millions of people. Current audits measure what they say to an average user, but their...
arXiv:2608.24748v1 Announce Type: cross Abstract: How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixe...
arXiv:2606. 17286v1 Announce Type: cross Abstract: AI-enabled authoritarianism is not confined to autocracies.