New Conditions for Philosophers to Catch the Wave of Citizen Deliberation in the Age of Artificial Intelligence in advance
- URL: http://arxiv.org/abs/2609.15707v1
- Date: Mon, 14 Sep 2026 15:11:02 GMT
- Title: New Conditions for Philosophers to Catch the Wave of Citizen Deliberation in the Age of Artificial Intelligence in advance
- Abstract summary: The Democratic Commons project takes a fundamentally interdisciplinary approach-from philosophy to computer science-to evaluate AI systems.<n>The project is driven by the question of political bias: under what conditions can AI systems be used democratically within forms of citizen participation that are them- selves still largely experimental?
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Powerful technologies labeled ``AI''-without sufficient epistemic caution-are already reshaping political and private life, bringing both new dangers and new opportunities for citizen participation. These range from electoral and legislative engagement to the most ambitious form: political co-creation through citizens' assemblies. Large Language Models (LLMs) could support such processes through moderation, translation, facilitation, summarization, and writing assistance. But this potential remains largely unrealized. The Democratic Commons project takes a fundamentally interdisciplinary approach-from philosophy to computer science-to evaluate LLMs against five proposed democratic principles. At its core, the project is driven by the question of political bias: under what conditions can LLMs be used democratically within forms of citizen participation that are them- selves still largely experimental? Addressing these socio-technical questions requires grounding in political theory and, more broadly, in philosophy-disciplines that provide the normative frameworks without which the democratic evaluation of AI systems cannot be mean- ingfully conducted.
Related papers
- Can AI mediation improve democratic deliberation? [10.125698716274286]
We ask whether and how artificial intelligence could help navigate the "trilemma" of broad participation, meaningful deliberation, and political equality.<n>We look at a large language model (LLM)-based system designed to help people with diverse viewpoints find common ground.<n>Ultimately, a range of empirical, technical, and theoretical advancements are needed to fully realize the promise of AI-mediated deliberation.
arXiv Detail & Related papers (2026-01-09T16:22:26Z) - Of the People, By the Algorithm: How AI Transforms Democratic Representation [0.0]
AI technologies are transforming democratic representation, focusing on citizen participation and algorithmic decision-making.<n>Social media platforms' AI-driven algorithms currently mediate much political discourse.<n>The emergence of Mass Online Deliberation platforms suggests possibilities for scaling up meaningful citizen participation.<n>Algorithmic Decision-Making systems promise more efficient policy implementation but face limitations in handling complex political trade-offs.
arXiv Detail & Related papers (2025-08-26T13:54:17Z) - Advancing Science- and Evidence-based AI Policy [163.43609502905707]
This paper tackles the problem of how to optimize the relationship between evidence and policy to address the opportunities and challenges of AI.<n>An increasing number of efforts address this problem by often either (i) contributing research into the risks of AI and their effective mitigation or (ii) advocating for policy to address these risks.
arXiv Detail & Related papers (2025-08-02T23:20:58Z) - Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models [72.89977583150748]
We propose a novel methodology to assess how Large Language Models align with broader geopolitical value systems.<n>We find that LLMs generally favor democratic values and leaders, but exhibit increases favorability toward authoritarian figures when prompted in Mandarin.
arXiv Detail & Related papers (2025-06-15T07:52:07Z) - Aligning Trustworthy AI with Democracy: A Dual Taxonomy of Opportunities and Risks [26.916552909766118]
This paper introduces a dual taxonomy to evaluate AI's complex relationship with democracy.<n>TheAIRD taxonomy identifies how AI can undermine core democratic principles such as autonomy, fairness, and trust.<n>The AIPD taxonomy highlights AI's potential to enhance transparency, participation, efficiency, and evidence-based policymaking.
arXiv Detail & Related papers (2025-05-19T10:51:08Z) - Political Neutrality in AI Is Impossible- But Here Is How to Approximate It [97.59456676216115]
We argue that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions.<n>We use the term "approximation" of political neutrality to shift the focus from unattainable absolutes to achievable, practical proxies.
arXiv Detail & Related papers (2025-02-18T16:48:04Z) - Political-LLM: Large Language Models in Political Science [159.95299889946637]
Large language models (LLMs) have been widely adopted in political science tasks.<n>Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
arXiv Detail & Related papers (2024-12-09T08:47:50Z) - Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground [51.85131234265026]
I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms.<n>I question the current narrow prioritization in AI ethics of moral innovation over moral preservation.
arXiv Detail & Related papers (2024-12-06T15:36:13Z) - Large Language Models in Politics and Democracy: A Comprehensive Survey [0.0]
Large language models (LLMs) offer potential across various domains, including policymaking, political communication, analysis, and governance.<n>LLMs offer opportunities to enhance efficiency, inclusivity, and decision-making in political processes.<n>They also present challenges related to bias, transparency, and accountability.
arXiv Detail & Related papers (2024-12-01T15:23:34Z) - Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work [10.45161883458636]
This position paper argues that effectively "democratizing AI" requires democratic governance and alignment of AI.<n>We provide a "Democracy Levels" framework and associated tools to explore what increasingly democratic AI might look like.
arXiv Detail & Related papers (2024-11-14T06:37:45Z) - Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysis [48.14390493099495]
How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance.<n>This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos.
arXiv Detail & Related papers (2024-09-15T03:17:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.