# Recognition Without Authorization: LLMs and the Moral Order of Online Advice

arXiv: https://arxiv.org/abs/2604.22143 · HTML: https://mlsanity.com/paper/2604.22143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Algorithmic Monoculture and Social Welfare](https://mlsanity.com/paper/2101.05853/) (2021)
- [Normative Evaluation of Large Language Models with Everyday Moral Dilemmas](https://mlsanity.com/paper/2501.18081/) (2025)
- [Exploring LGBTQ+ Bias in Generative AI Answers across Different Country and Religious Contexts](https://mlsanity.com/paper/2407.03473/) (2024)

## Abstract (arXiv)

Large language models are increasingly used to mediate everyday interpersonal dilemmas, yet how their advisory defaults interact with the concentrated moral orders of specific communities remains poorly understood. This article compares four assistant-style LLMs with community-endorsed advice on 11,565 posts from r/relationship_advice, using the subreddit as a concentrated, vote-ratified moral formation whose prescriptive clarity makes divergence measurable. Across models, LLMs identify many of the same dynamics as human commenters, but are markedly less likely to convert that recognition into directive authorization for action. The gap is sharpest where community consensus is strongest: on high-consensus posts involving abuse or safety threats, models recommend exit at roughly half the human rate while maintaining elevated levels of hedging, validation, and therapeutic framing. The article describes this pattern as recognition without authorization: the capacity to register harm while withholding socially ratified permission for consequential action. This divergence is not incidental but structural: a portable advisory style that remains validating, risk-averse, and weakly directive across contexts. Safety alignment is one plausible contributor to this pattern, alongside training-data averaging and broader assistant design. The article argues that model divergence can be reframed from a technical error to a way of seeing what standardized assistant norms flatten when they encounter situated moral worlds.