Radical Moral Disagreements (RMDs) are highly polarising topics that are increasingly censored in everyday life, with growing evidence suggesting that this polarisation carries measurable costs to public mental health. To address these challenges, some researchers have proposed Large Language Models (LLMs) as a means to support more democratic deliberation and better moral reasoning. Yet existing tools are poorly calibrated to help people navigate RMDs, because of their intense and divisive characteristics. This paper introduces CONSIDER, a prototype for a one-to-one AI tool for RMD navigation. Drawing on Mill's account of the epistemic value of disagreement, CONSIDER aims at value clarification through structured disagreement with an opposing LLM-generated opinion. We describe CONSIDER's design logic and analyse potential risks posed by such tools to guide future development.

翻译：根本性道德分歧（Radical Moral Disagreements，RMDs）是高度两极分化的话题，在日常生活中日益受到审查，且越来越多证据表明这种两极分化对公众心理健康造成了可量化的损害。为应对这些挑战，部分研究者提出将大型语言模型（LLMs）作为支持更民主的协商与更优道德推理的工具。然而，现有工具由于RMDs的激烈性与分裂性特征，难以有效帮助人们应对这类分歧。本文介绍了CONSIDER——一个用于引导RMDs的一对一AI工具原型。基于密尔关于分歧认知价值的理论，CONSIDER旨在通过用户与LLM生成的对立观点进行结构化辩论，实现价值澄清。我们阐述了CONSIDER的设计逻辑，并分析了此类工具可能带来的潜在风险，以指导未来开发方向。