用AI生成内容可降低网络社区对阿片类药物滥用的偏见。
Exposure to Content Written by Large Language Models Can Reduce Stigma Around Opioid Use Disorder in Online Communities
- 用大模型生成回复，替代真人或无回复
- 两种实验中，大模型组对药物治疗态度最无偏见
- 适合想改善线上健康讨论氛围的研究者
广泛存在的污名化，无论在线下还是线上，都阻碍了阿片类药物使用障碍（OUD）的减害努力。这种污名主要针对经临床批准的药物治疗（MAT）、患者本人以及疾病本身。鉴于人工智能技术在促进健康公平和推动共情对话方面的潜力，本研究探讨大语言模型（LLMs）是否有助于缓解在线社区中的OUD相关污名。我们进行了预注册的随机对照实验，参与者阅读由大模型生成、人类撰写或无回复的OUD求助内容回应。实验分两种设置：单次阅读（N=2,141）或连续14天重复阅读（N=107）。结果显示，在两种设置下，接触大模型生成内容的参与者对MAT的污名化态度最低。研究揭示了通过大模型实现包容性在线对话的策略，表明其可作为基于教育的干预手段，促进积极态度并提升人们对MAT的接受度。
原文摘要 · Abstract (English)
Widespread stigma, both in the offline and online spaces, acts as a barrier to harm reduction efforts in the context of opioid use disorder (OUD). This stigma is prominently directed towards clinically approved medications for addiction treatment (MAT), people with the condition, and the condition itself. Given the potential of artificial intelligence based technologies in promoting health equity, and facilitating empathic conversations, this work examines whether large language models (LLMs) can help abate OUD-related stigma in online communities. To answer this, we conducted a series of pre-registered randomized controlled experiments, where participants read LLM-generated, human-written, or no responses to help seeking OUD-related content in online communities. The experiment was conducted under two setups, i.e., participants read the responses either once (N = 2,141), or repeatedly for 14 days (N = 107). We found that participants reported the least stigmatized attitudes toward MAT after consuming LLM-generated responses under both the setups. This study offers insights into strategies that can foster inclusive online discourse on OUD, e.g., based on our findings LLMs can be used as an education-based intervention to promote positive attitudes and increase people's propensity toward MAT.
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