Fair and Diverse Allocation of Scarce Resources

Speaker:

Lulu Kang, associate professor of applied mathematics, Illinois Institute of Technology

Description:

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 U.S. and other parts of the world in large scale. Many poor communities and minority groups have been shown to be much more vulnerable than the rest. To provide sufficient vaccine and medical resources and effectively stop the further spreading of the pandemic, the probability of an individual receiving a unit of treatment should be independent of their demographic group but only conditional on the individual’s exposure rate. In this article, we integrate different aspects of resource allocation and seek a synergistic intervention strategy that vaccinate the vulnerable populations with higher priority. This prevention-centered strategy is a trade-off between geographical coverage and social group fairness. The proposed principle can be applied to other scarce resources and social benefits allocation.  

Computational Mathematics and Statistics