Access to sexual and reproductive health information remains a challenge in many communities globally, due to cultural taboos and limited availability of healthcare providers. Public health organizations are increasingly turning to Large Language Models (LLMs) to improve access to timely and personalized information. However, recent HCI scholarship indicates that significant challenges remain in incorporating context awareness and mitigating bias in LLMs. In this paper, we study the development of a culturally-appropriate LLM-based chatbot for reproductive health with underserved women in urban India. Through user interactions, focus groups, and interviews with multiple stakeholders, we examine the chatbot's response to sensitive and highly contextual queries on reproductive health. Our findings reveal strengths and limitations of the system in capturing local context, and complexities around what constitutes "culture". Finally, we discuss how local context might be better integrated, and present a framework to inform the design of culturally-sensitive chatbots for community health.

翻译：在全球许多社区中，由于文化禁忌和医疗保健提供者有限，获取性与生殖健康信息仍然面临挑战。公共卫生组织日益转向采用大型语言模型（LLMs）来改善及时、个性化信息的可及性。然而，近期人机交互研究表明，在LLMs中融入情境认知和减轻偏见方面仍存在重大挑战。本文研究了为印度城市中服务不足的女性开发一个基于LLM、文化适宜的生殖健康聊天机器人的过程。通过用户交互、焦点小组及与多方利益相关者的访谈，我们检验了聊天机器人对生殖健康领域敏感且高度情境化查询的响应。我们的研究结果揭示了该系统在捕捉本地情境方面的优势与局限，以及围绕"文化"构成的复杂性。最后，我们探讨了如何更好地整合本地情境，并提出了一个框架，为社区健康领域文化敏感性聊天机器人的设计提供参考。