Paper Detail
William Hohnen-Ford, Sarah Chen, Kathryn B. Francis, Madeline G. Reinecke, Ilina Singh, David Lyreskog
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.
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@article{hohnenford2026can,
title = {Can Generative AI help people navigate Radical Moral Disagreements? The CONSIDER prototype},
author = {William Hohnen-Ford and Sarah Chen and Kathryn B. Francis and Madeline G. Reinecke and Ilina Singh and David Lyreskog},
year = {2026},
abstract = {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 char},
url = {https://arxiv.org/abs/2605.31574},
keywords = {cs.HC},
eprint = {2605.31574},
archiveprefix = {arXiv},
}
{}