Date Posted: 4/27/2021

A paper by Hoda Heidari and Jon Kleinberg, "Allocating Opportunities in a Dynamic Model of Intergenerational Mobility," has received one of the Best Paper Awards at the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT). Heidari is a former post-doctoral associate in the Computer Science department at Cornell, where she was supervised by Kleinberg, Karen Levy (Information Science), and Solon Barocas (Information Science). Heidari is currently an assistant professor at Carnegie Mellon University with joint appointments in the Machine Learning Department and the Institute for Software Research. Kleinberg is Tisch University Professor in the departments of Computer Science and Information Science.

This is the second consecutive year that a Cornell paper has received one of the FAccT conference's best paper awards. Last year, a paper by doctoral candidate Kate Donahue and Jon Kleinberg, "Fairness and Utilization in Allocating Resources with Uncertain Demand," received a Best Paper Award at the 2020 ACM FAccT Conference. Read more about the paper at this link.

Addressing this year's winning paper, Heidari and Kleinberg describe their findings in "Allocating Opportunities in a Dynamic Model of Intergenerational Mobility," this way:

Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. We develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status—a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status.

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