用AI扩大民主讨论，让边缘群体更平等参与。
The Almost Intelligent Revolution: Options for Scaling Up Deliberation and Empowering People with AI
- 基于语言学理论优化AI辅助对话机制
- 降低精英语体影响，提升包容性参与
- 适合关注AI与民主、语言公平的研究者
大型语言模型（LLMs）在公共讨论中的日益突出，为民主协商带来机遇与挑战。尽管红队测试能缓解特定风险，但语言局限、偏见及模型的阿谀倾向仍存。本文探讨如何利用LLM显著扩展并民主化协商过程，尤其促进包容性，赋能传统边缘群体。结合系统功能语言学，分析用户差异（如社会人口特征）和语言使用差异（如交际功能）如何影响AI支持下的参与。通过实证研究展示AI在论点支架、获取便利性和削弱排他性语言规范方面的潜力。同时警示过度宣传会引发不切实际期待，而低估则错失机会。文章提出未来研究方向，旨在最大化AI参与的民主价值，并嵌入伦理保障以防止语言不平等再生产。
原文摘要 · Abstract (English)
The increasing prominence of Large Language Models (LLMs) in public discourse presents both opportunities and challenges for democratic deliberation. While red teaming strategies help mitigate specific risks, broader concerns persist regarding linguistic constraints, biases, and the sycophantic tendencies of LLMs. This chapter explores how LLMs can be used to significantly scale up and democratise deliberation, particularly in fostering inclusivity and empowering traditionally marginalised groups. Drawing on concepts from Systemic-Functional Linguistics, the chapter examines how variations across language users (for example, with respect to socio-demographic groups) and across language use (for example, with respect to communicative functions) shape participation in AI-supported deliberation. The chapter presents AI-driven deliberation studies and assesses their potential to scaffold argumentation, enhance access, and reduce the influence of exclusionary linguistic norms and biases which are embedded in prestigious registers. At the same time, the chapter cautions against both overclaiming, which leads to unrealistic expectations, and underclaiming, which risks missed opportunities for AI-assisted engagement. The chapter concludes by identifying future research directions to maximise the democratic potential of AI-assisted participation while embedding ethical safeguards to counteract the reproduction of linguistic inequalities.
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