Monday, November 8, 2021 - 4:00pm to 5:00pm
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LIDS Seminar Series
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Poverty is a prevalent issue in the United States, impacting over twenty percent of the population. Given poverty's detrimental impact on communities, there is a pressing need to predict hardship as well as provide timely and effective interventions. Despite the multi-dimensional nature of disadvantage -- ranging from housing instability and lack of access to physical and mental health care to educational support and food security -- agencies providing services often specialize in a single area, and seldom coordinate with other agencies. This decentralized nature of assistance, policy-makers and advocates argue, leads to individuals’ needs not being addressed holistically, and may perpetuate cycles of poverty.
In this talk, we focus on two key questions: in the first part, we study the problem of evaluating such decentralized systems of provision of social services. Under natural assumptions on peoples’ utilities for services as well as fairness considerations, we find that decentralized systems of allocation, even when optimal, can have a large gap when compared to optimal global provisions. We then consider coordination mechanisms, inspired by insights from work with California’s Sonoma County system, and theoretically examine whether these interventions can reduce gaps between local and global systems of provision. In the second part of the talk, we discuss recent work on predicting poverty. We evaluate popular conjectures around what makes poverty challenging to predict, and provide evidence that a key issue may be how we measure poverty and set guidelines for determining eligibility for receiving services. We will then discuss research directions where algorithmic and computational techniques can contribute to policy and advocacy efforts.
Rediet Abebe is an Assistant Professor of Computer Science at the University of California, Berkeley and a Junior Fellow at the Harvard Society of Fellows. Abebe holds a Ph.D. in computer science from Cornell University and graduate degrees in mathematics from Harvard University and the University of Cambridge. Her research is in artificial intelligence and algorithms, with a focus on inequality and distributive justice concerns. Abebe served as a Program Co-Chair for the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21) and currently serves on the Executive Committee for the conference. She also co-founded and co-organizes the related MD4SG research initiative. Her dissertation received the 2020 ACM SIGKDD Dissertation Award and an honorable mention for the ACM SIGEcom Dissertation Award for offering the foundations of this emerging research area. She has been honored in the MIT Technology Reviews’ 35 Innovators Under 35 and the Bloomberg 50 list as a one to watch. Abebe also co-founded and serves on the Board of Directors for Black in AI, a non-profit organization tackling equity issues in AI. Her work is influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.