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<p>Democratic discourse is increasingly strained by social risk and deep ideological division, leaving citizens reluctant to engage across disagreement even when such engagement is essential for collective epistemic progress. In this paper, we argue that large language models (LLMs) can function as tools for navigating difficult disagreements by creating <i>low-risk epistemic spaces</i>, or environments where agents can test reasons, rehearse arguments, and explore opposing perspectives without incurring the interpersonal costs that often derail human-to-human exchange. We distinguish between two forms of difficult disagreement – <i>socially fraught</i> and <i>deep</i> – and show how LLMs may mitigate the epistemic hazards of each. In socially fraught contexts, LLMs can reduce reputational and emotional vulnerability while redistributing epistemic labor that otherwise disproportionately falls on the marginalized. In cases of deep disagreement, LLMs can flag divergent frameworks, scaffold meta-cognitive awareness, and mediate the transfer of information without triggering individually rational incentives to misinform. While engaging with LLMs will never be a proper substitute for democratic dialogue, we contend that carefully designed LLMs can lower the barriers to more productive engagement across difference under non-ideal conditions.</p>

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Bridging the Divide: Using LLMs to Reshape Difficult Disagreements

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月16日

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  • Matthew Willis,
  • Bennett Meacham,
  • Peyton Tarle

摘要

Democratic discourse is increasingly strained by social risk and deep ideological division, leaving citizens reluctant to engage across disagreement even when such engagement is essential for collective epistemic progress. In this paper, we argue that large language models (LLMs) can function as tools for navigating difficult disagreements by creating low-risk epistemic spaces, or environments where agents can test reasons, rehearse arguments, and explore opposing perspectives without incurring the interpersonal costs that often derail human-to-human exchange. We distinguish between two forms of difficult disagreement – socially fraught and deep – and show how LLMs may mitigate the epistemic hazards of each. In socially fraught contexts, LLMs can reduce reputational and emotional vulnerability while redistributing epistemic labor that otherwise disproportionately falls on the marginalized. In cases of deep disagreement, LLMs can flag divergent frameworks, scaffold meta-cognitive awareness, and mediate the transfer of information without triggering individually rational incentives to misinform. While engaging with LLMs will never be a proper substitute for democratic dialogue, we contend that carefully designed LLMs can lower the barriers to more productive engagement across difference under non-ideal conditions.