Tootfinder

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions
Kaixuan Wang, Jason T. Jacques, Chenxin Diao
https://arxiv.org/abs/2506.22941

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

HRIPBench: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs
Kaixuan Wang, Chenxin Diao, Jason T. Jacques, Zhongliang Guo, Shuai Zhao
https://arxiv.org/abs/2507.21815

HRIPBench: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs
Millions of individuals' well-being are challenged by the harms of substance use. Harm reduction as a public health strategy is designed to improve their health outcomes and reduce safety risks. Some large language models (LLMs) have demonstrated a decent level of medical knowledge, promising to address the information needs of people who use drugs (PWUD). However, their performance in relevant tasks remains largely unexplored. We introduce HRIPBench, a benchmark designed to evaluate LLM's accu…