面向多数世界健康领域的文化适配型对话式AI：一项针对拉丁美洲公民与专业人士的探索性研究
Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America
AI总结：
本研究针对多数世界健康对话式AI的文化适配问题，以拉丁美洲为对象通过自下而上的参与式定性研究，揭示基层文化的多元交织性，提出多元世界健康对话式AI框架，补充了现有自上而下的技术路径。
摘要：
在多数世界地区，利用对话式AI（CAI）助力健康领域的需求具有合理依据，但要发挥实效，CAI必须在多元文化与语言语境中做出恰当回应。因此，我们需要设法解决当前大语言模型（LLM）未纳入全球众多生活经验的问题。目前已有多种聚焦自上而下方法、扩充训练数据的研究进展。在本文中，我们旨在通过基于拉丁美洲参与式工作坊中收集的定性数据的自下而上、扎根本地的方法，对这些研究形成补充。我们的目标是从以人为本的角度，深入理解以下内容：a）数字健康领域中文化错位的潜在领域；b）地区对健康类聊天机器人的看法；c）构建文化适配型CAI的策略，研究重点为尚未被充分研究的拉丁美洲语境。我们的研究结果表明，学术层面对文化概念的界定在基层场景中失去意义，技术需要纳入更宽泛的框架——该框架涵盖经济、政治、地理和本地物流与文化经验相交织的作用方式。为此，我们提出了“多元世界健康对话式AI”框架，该框架指出，我们可能需要更强的关系性与包容性，而非仅仅增加数据量。
英文摘要：
There is justifiable interest in leveraging conversational AI (CAI) for health across the majority world, but to be effective, CAI must respond appropriately within culturally and linguistically diverse contexts. Therefore, we need ways to address the fact that current LLMs exclude many lived experiences globally. Various advances are underway which focus on top-down approaches and increasing training data. In this paper, we aim to complement these with a bottom-up locally-grounded approach based on qualitative data collected during participatory workshops in Latin America. Our goal is to construct a rich and human-centred understanding of: a) potential areas of cultural misalignment in digital health; b) regional perspectives on chatbots for health and c)strategies for creating culturally-appropriate CAI; with a focus on the understudied Latin American context. Our findings show that academic boundaries on notions of culture lose meaning at the ground level and technologies will need to engage with a broader framework; one that encapsulates the way economics, politics, geography and local logistics are entangled in cultural experience. To this end, we introduce a framework for 'Pluriversal Conversational AI for Health' which allows for the possibility that more relationality and tolerance, rather than just more data, may be called for.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
