mlsanityData to 14 Oct 2026

The Almost Intelligent Revolution: Options for Scaling Up Deliberation and Empowering People with AI

arXiv 2606.19864 ↗First posted 18 Jun 2026cs.CLcited by 0

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Abstract (arXiv)

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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References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  3. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 836
  4. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 729
  5. 2020Artificial Intelligence, Values and Alignmentcited by 448
  6. 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 439
  7. 2024Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?cited by 232
  8. 2018Achieving Human Parity on Automatic Chinese to English News Translationcited by 119
  9. 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Failcited by 38
  10. 2025Neuro-Symbolic AI in 2024: A Systematic Reviewcited by 38
  11. 2020A Set of Recommendations for Assessing Human-Machine Parity in Language Translationcited by 34
  12. 2025Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Dividecited by 4

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  2. 2025Artificial Authority: From Machine Minds to Political Alignments. An Experimental Analysis of Democratic and Autocratic Biases in Large-Language Models
  3. 2026The Deliberative Deficit: An Empirical Critique of LLMs in Democratic Discourse
  4. 2026Evaluating the Capabilities of LLMs for Persuasive Dialogue
  5. 2025Stop saying LLM: Large Discourse Models (LDM) and Artificial Discursive Agent (ADA)?
  6. 2026Mapping the Emerging Social Science of Large Language Models
  7. 2026AI Knows When It's Being Watched: Functional Strategic Action and Contextual Register Modulation in Large Language Models
  8. 2025Multiple LLM Agents Debate for Equitable Cultural Alignment