Fair and diverse allocation of scarce resources

Highlights

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We developed a fair-diverse scarce resource allocation optimization framework.

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We considered a trade-off between Geographical Diversity and Social group Fairness.

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We proposed a tuning approach to find an optimum range for the trade-off parameter.

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We evaluated the performance of our proposed approach using real COVID-19 datasets.

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We demonstrated the allocation results for three major segregated cities in the U.S.

Abstract

We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of this work, the COVID-19 pandemic is still spreading in the U.S. and other parts of the world on large scale. Many poor communities and minority groups are much more vulnerable than the rest. To provide sufficient vaccine and medical resources to all residents and effectively stop the further spreading of the pandemic, the average medical resources per capita of a community should be independent of the community's demographic features but only conditional on the exposure rate to the disease. In this article, we integrate different aspects of resource allocation and create a synergistic intervention strategy that gives vulnerable populations higher priority in medical resource distribution. This prevention-centered strategy seeks a balance between geographical coverage and social group fairness. The proposed principle can be applied to other scarce resources and social benefits allocation.