During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access information portal is highly desirable. It helps with disease prevention, public health planning, and misinformation mitigation. However, creating such an information portal is challenging because 1) domain expertise is required to identify and curate credible and intelligible content, 2) the information needs to be updated promptly in response to the fast-changing environment, and 3) the information should be easily accessible by the general public; which is particularly difficult when most people do not have the domain expertise about the crisis. In this paper, we presented an expert-sourcing framework and created Jennifer, an AI chatbot, which serves as a credible and easy-to-access information portal for individuals during the COVID-19 pandemic. Jennifer was created by a team of over 150 scientists and health professionals around the world, deployed in the real world and answered thousands of user questions about COVID-19. We evaluated Jennifer from two key stakeholders' perspectives, expert volunteers and information seekers. We first interviewed experts who contributed to the collaborative creation of Jennifer to learn about the challenges in the process and opportunities for future improvement. We then conducted an online experiment that examined Jennifer's effectiveness in supporting information seekers in locating COVID-19 information and gaining their trust. We share the key lessons learned and discuss design implications for building expert-sourced and AI-powered information portals, along with the risks and opportunities of misinformation mitigation and beyond.
翻译:在诸如COVID-19大流行病等公共卫生危机期间,一个可信和容易获取的信息门户非常可取,它有助于疾病预防、公共卫生规划和减少错误信息;然而,建立这样一个信息门户具有挑战性,因为:(1) 需要领域专门知识来查明和整理可信和易于理解的内容;(2) 需要根据快速变化的环境迅速更新信息;(3) 公众应容易获取信息;当大多数人没有关于危机的域内专门知识时,这种信息尤其困难;在本文件中,我们提出了一个专家采购框架,并创建了珍妮弗,即一个AI聊天室,作为COVID-19大流行病期间个人一个可信和容易获取的信息门户;Jennifer是由世界各地150多名科学家和卫生专业人员组成的团队创建的,以识别和整理可靠内容;(2) 需要及时更新信息,以适应快速变化环境;(3) 公众应当能够从两个主要利益攸关方、专家志愿者和信息寻求者的角度对Jenniferfer系统进行评估;我们首先采访了为Jennifer提供协作的专家,以了解进程中的挑战和今后改进的机会;然后,我们进行了在线实验,以了解在建设国际...公司数据库方面的重要信息方面的风险,并探讨在设计中获取风险。