Ethical Implications of Artificial Intelligence in Vaccine Equity: Exploring Vaccine Distribution Planning and Scheduling in Pandemics in Low-Middle-Income-Countries

This dataset comprises a witness seminar transcript exploring the effectiveness of AI-based distribution planning and scheduling systems in ensuring equitable vaccine distribution in Low- and Middle-Income Countries (LMICs). The dataset includes discussions from multiple expert participants, including public health professionals, AI researchers, medical practitioners, and ethicists, who shared their perspectives based on their experiences during the COVID-19 vaccine rollout. The research questions focused on identifying systemic inequities in vaccine distribution, the role of AI in scheduling and outreach, and ethical considerations regarding data privacy, bias, and accessibility. Participants discussed how digital platforms such as India’s CoWIN app improved vaccine tracking and administration but also highlighted key challenges, such as limited smartphone access, digital illiteracy, and exclusion of marginalized groups (e.g., transgender individuals, persons with disabilities, and rural communities). Experts noted that AI-based systems require a hybrid approach, combining technological solutions with ground-level outreach by community health workers to ensure equity. The thematic analysis of the transcript reveals recurring themes such as AI's role in optimizing vaccine delivery, ethical concerns regarding data privacy, digital literacy barriers, and challenges in reaching underserved populations. Participants emphasized that while AI can enhance efficiency and tracking, it also risks reinforcing existing inequities if data biases and structural healthcare disparities are not addressed. Discussions further examined the importance of community engagement, transparency, and policy interventions in refining AI-driven vaccine distribution models. This dataset provides rich qualitative insights into the intersection of technology, ethics, and health equity, offering valuable guidance for policymakers, researchers, and global health organizations aiming to improve AI-driven public health interventions in LMICs.本数据集包含一场见证式研讨会的转录文本，旨在探讨基于人工智能（AI）的配送规划与调度系统在保障中低收入国家（Low- and Middle-Income Countries, LMICs）疫苗公平分配方面的有效性。本数据集收录了多位专家参与者的讨论内容，涵盖公共卫生从业者、人工智能研究者、临床医师与伦理学家，他们均结合自身在新冠疫苗接种推广期间的实践经验分享了相关见解。本次研讨会的研究议题聚焦于三大核心方向：一是识别疫苗分配中的系统性不公平现象；二是人工智能在疫苗调度与 outreach推广工作中的作用；三是数据隐私、算法偏见与医疗服务可及性相关的伦理考量。与会嘉宾探讨了印度CoWIN应用程序等数字平台如何优化疫苗追踪与接种管理，但同时也指出了若干核心挑战：包括智能手机普及率不足、数字素养匮乏，以及弱势群体（如跨性别群体、残障人士与农村社区）被排除在外的问题。专家指出，基于人工智能的系统需采用混合模式，将技术解决方案与社区卫生工作者的一线 outreach服务工作相结合，以保障分配公平。对本次转录文本的主题分析显示，多个核心主题反复出现：包括人工智能在优化疫苗配送中的作用、数据隐私相关的伦理担忧、数字素养壁垒，以及服务欠发达群体的重重挑战。与会嘉宾强调，尽管人工智能能够提升效率与追踪能力，但如果不解决数据偏见与结构性医疗资源失衡问题，该技术反而可能加剧既有的不公平现象。本次研讨还进一步探讨了社区参与、透明度与政策干预在优化人工智能驱动的疫苗分配模式中的重要性。本数据集为技术、伦理与健康公平三者的交叉领域提供了丰富的质性研究视角，可为旨在优化中低收入国家人工智能驱动的公共卫生干预措施的政策制定者、研究者与全球卫生组织提供宝贵的参考指引。