Powering an AI Chatbot with Expert Sourcing to Support Credible Health Information Access
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2023.
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Citing papers per year: 2023: 3; 2024: 6.
9 papers.
- 2024How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensionscited by 51
- 2024ComPeer: A Generative Conversational Agent for Proactive Peer Supportcited by 24
- 2024Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seekingcited by 13
- 2023Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomescited by 10
- 2024CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patientscited by 9
- 2023Inform the uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbotcited by 5
- 2024HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settingscited by 4
- 2024Fine-Tuning LLMs for Reliable Medical Question-Answering Servicescited by 4
- 2023Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Modelcited by 1
- 2024Fine-Tuning LLMs for Reliable Medical Question-Answering Services21 Oct 2024
- 2024HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings17 Oct 2024
- 2024ComPeer: A Generative Conversational Agent for Proactive Peer Support25 Jul 2024
- 2024How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions21 Jun 2024
- 2024Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking8 Feb 2024
- 2024CataractBot: An LLM-Powered Expert-in-the-Loop Chatbot for Cataract Patients7 Feb 2024
- 2023Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model17 Sep 2023
- 2023Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes8 Jul 2023
- 2023Inform the uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbot2 Feb 2023