# Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategies

arXiv: https://arxiv.org/abs/2602.08187 · HTML: https://mlsanity.com/paper/2602.08187/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 118 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
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- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://mlsanity.com/paper/2306.04140/) (2023)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://mlsanity.com/paper/2407.04069/) (2024)
- [On Learning to Summarize with Large Language Models as References](https://mlsanity.com/paper/2305.14239/) (2023)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions](https://mlsanity.com/paper/2107.07015/) (2021)
- [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023)
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- [LLM-Generated Black-box Explanations Can Be Adversarially Helpful](https://mlsanity.com/paper/2405.06800/) (2024)
- [The Limits of Global Inclusion in AI Development](https://mlsanity.com/paper/2102.01265/) (2021)
- [Auditing for Human Expertise](https://mlsanity.com/paper/2306.01646/) (2023)
- [Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms](https://mlsanity.com/paper/2101.10367/) (2021)
- [Characterizing and modeling harms from interactions with design patterns in AI interfaces](https://mlsanity.com/paper/2404.11370/) (2024)
- [The AI Revolution in Education: Will AI Replace or Assist Teachers in Higher Education?](https://mlsanity.com/paper/2305.01185/) (2023)
- ["Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism](https://mlsanity.com/paper/2501.17299/) (2025)
- [The AI-DEC: A Card-based Design Method for User-centered AI Explanations](https://mlsanity.com/paper/2405.16711/) (2024)
- ["Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic Decisions in Contentious Public Policy Domains](https://mlsanity.com/paper/2204.10814/) (2022)

## Abstract (arXiv)

Peer-run organizations (PROs) provide critical, recovery-based behavioral health support rooted in lived experience. As large language models (LLMs) enter this domain, their scale, conversationality, and opacity introduce new challenges for situatedness, trust, and autonomy. Partnering with Collaborative Support Programs of New Jersey (CSPNJ), a statewide PRO in the Northeastern United States, we used comicboarding, a co-design method, to conduct workshops with 16 peer specialists and 10 service users exploring perceptions of integrating an LLM-based recommendation system into peer support. Findings show that depending on how LLMs are introduced, constrained, and co-used, they can reconfigure in-room dynamics by sustaining, undermining, or amplifying the relational authority that grounds peer support. We identify opportunities, risks, and mitigation strategies across three tensions: bridging scale and locality, protecting trust and relational dynamics, and preserving peer autonomy amid efficiency gains. We contribute design implications that center lived-experience-in-the-loop, reframe trust as co-constructed, and position LLMs not as clinical tools but as relational collaborators in high-stakes, community-led care.