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Public perceptions of COVID-19 vaccines are critical in reaching protective levels of herd immunity. Vaccine skepticism has always been relatively high in Germany, and surveys suggest that over the course of the pandemic, enthusiasm for the COVID-19 vaccine has dropped. Looking at the period just prior to the approval of the Pfizer/BioNTech and Moderna vaccines in Germany in the latter half of 2020, this paper aims to assess the reasons for and against COVID-19 vaccine uptake among residents of Germany, and to provide in-depth qualitative data to better understand and address concerns surrounding the safety and efficacy of a COVID-19 vaccine. Our findings indicate that there is widespread trust in German institutions and health experts to provide a safe vaccine for those who need it most. However, interviewees also point to the need for more information and the centrality of support from trusted medical authorities in making individual vaccination decisions. We also present the complexity of individual positions on vaccination, and suggest that vaccine hesitancy in relation to COVID-19 needs to be understood as a nuanced, and socially malleable, territory. This indicates that the goal of a vaccination campaign is not only achieving 'herd immunity,' but also a social endorsement of the collaborative effort that is required for a vaccine to be successful. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning are central components of today's medical environment. The fairness of AI, i.e. the ability of AI to be free from bias, has repeatedly come into question. This study investigates the diversity of members of academia whose scholarship poses questions about the fairness of AI. The articles that combine the topics of fairness, artificial intelligence, and medicine were selected from Pubmed, Google Scholar, and Embase using keywords. Eligibility and data extraction from the articles were done manually and cross-checked by another author for accuracy. Articles were selected for further analysis, cleaned, and organized in Microsoft Excel; spatial diagrams were generated using Public Tableau. Additional graphs were generated using Matplotlib and Seaborn. Linear and logistic regressions were conducted using Python to measure the relationship between funding status, number of citations, and the gender demographics of the authorship team. We identified 375 eligible publications, including research and review articles concerning AI and fairness in healthcare. Analysis of the bibliographic data revealed that there is an overrepresentation of authors that are white, male, and are from high-income countries, especially in the roles of first and last author. Additionally, analysis showed that papers whose authors are based in higher-income countries were more likely to be cited more often and published in higher impact journals. These findings highlight the lack of diversity among the authors in the AI fairness community whose work gains the largest readership, potentially compromising the very impartiality that the AI fairness community is working towards., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alberto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)