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.