Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

TL;DR

This paper explores how Large Language Models can be responsibly designed to support harm reduction efforts for People Who Use Drugs, emphasizing stakeholder collaboration, ethical considerations, and practical design pathways.

Contribution

It provides empirical insights and practical design considerations for developing LLM-based tools tailored to harm reduction, addressing socio-technical challenges and stakeholder needs.

Findings

LLMs can reduce information barriers for PWUD

Designing ethically aligned LLMs is crucial

Stakeholder collaboration enhances system safety

Abstract

Access to accurate and actionable harm reduction information can directly impact the health outcomes of People Who Use Drugs (PWUD), yet existing online channels often fail to meet their diverse and dynamic needs due to limitations in adaptability, accessibility, and the pervasive impact of stigma. Large Language Models (LLMs) present a novel opportunity to enhance information provision, but their application in such a high-stakes domain is under-explored and presents socio-technical challenges. This paper investigates how LLMs can be responsibly designed to support the information needs of PWUD. Through a qualitative workshop involving diverse stakeholder groups (academics, harm reduction practitioners, and an online community moderator), we explored LLM capabilities, identified potential use cases, and delineated core design considerations. Our findings reveal that while LLMs can address some existing information barriers (e.g., by offering responsive, multilingual, and potentially less stigmatising interactions), their effectiveness is contingent upon overcoming challenges related to ethical alignment with harm reduction principles, nuanced contextual understanding, effective communication, and clearly defined operational boundaries. We articulate design pathways emphasising collaborative co-design with experts and PWUD to develop LLM systems that are helpful, safe, and responsibly governed. This work contributes empirically grounded insights and actionable design considerations for the responsible development of LLMs as supportive tools within the harm reduction ecosystem.