Ethical Implications of Artificial Intelligence in Vaccine Equity: Exploring Vaccine Distribution Planning and Scheduling in Pandemics in Low-Middle-Income-Countries
Description
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.
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Steps to reproduce
This dataset was collected using a witness seminar methodology, a structured qualitative approach that gathers expert perspectives on a specific issue. The study focused on the effectiveness of AI-based distribution planning and scheduling systems in ensuring equitable vaccine distribution in LMICs. Purposive sampling was used to recruit experts in public health, AI in healthcare, vaccine distribution, and bioethics. A semi-structured discussion guide was developed, covering topics such as AI's role in vaccine scheduling, ethical concerns (bias, privacy, digital exclusion), and barriers to equitable access. The seminar was conducted in a virtual and in-person hybrid format, recorded with participant consent, and transcribed verbatim for analysis. Ethical approval was obtained from the Institutional Ethics Committee (IEC), and all participants provided informed consent before the session. For data analysis, Taguatte software was used for coding and thematic extraction, following Braun & Clarke’s six-step framework. Member checking was conducted to ensure data accuracy, and inter-coder reliability was maintained through independent coding and multiple checks by guide(s). Findings were triangulated with existing literature to validate emerging themes. This rigorous approach ensures the dataset is reliable, ethically sound, and reproducible, providing valuable insights for AI-driven public health interventions in low-resource settings.