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

2026-06-18

AI Summary

This study addresses the susceptibility of large language models to linguistic bias and sycophantic tendencies in public deliberation, which can marginalize underrepresented groups and reinforce existing linguistic inequities. Integrating systemic functional linguistics with AI techniques, the work proposes an inclusive deliberation support mechanism that analyzes how pragmatic functions across diverse social groups shape participation. Empirical validation demonstrates the approach’s potential to enhance marginalized groups’ engagement, improve argument accessibility, and mitigate the dominance of elite linguistic norms. The research advances a design pathway for AI-mediated deliberative systems that simultaneously promotes democratic empowerment and upholds ethical safeguards.

Abstract

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.

Problem

• democratic deliberation
• Large Language Models
• linguistic bias
• inclusivity
• marginalised groups

Innovation

• Large Language Models
• democratic deliberation
• Systemic-Functional Linguistics
• linguistic bias
• inclusive participation