Background: When vaccines became first available during the Covid-19 pandemic, their demand significantly exceeded their supply. In consequence, the access to vaccines, initially, was distributed unequally. At the same time, governments started easing pandemic restrictions for vaccinated and recovered persons and restoring their freedoms since their risk of transmitting the virus is significantly reduced.Evidence: We show that restoring freedoms for vaccinated and recovered persons – while upholding restrictions for the rest of the population – is morally unfair during vaccine scarcity. Further, it may yield unintended side-effects, including perverse incentives, growing rifts in society, and the expansion of marginalization.Policy Options & Recommendations: We recommend accompanying easing for vaccinated and recovered individuals by mitigation measures for those who are neither vaccinated nor recovered. We propose, first, to temporarily lift the same restrictions for negative-tested individuals, as for vaccinated or recovered people. Second, the state must ensure broad and easy access to testing for everyone – free of charge.Conclusion: If done right, these mitigation measures create (at least temporarily) equal access to freedom for everybody – solving the moral problem of unfair access to freedoms and counteracting possible negative consequences.
Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic.
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