AI Seminar sponsored by Apple

Rediet Abebe -- Computational Interventions to Improve Access to Opportunity for Disadvantaged Populations

Abstract: Poverty and economic hardship are highly complex and dynamic phenomena. Due to the multi-dimensional nature of economic welfare, assistance programs aimed at improving access to opportunity for disadvantaged populations face challenges when relevant information about these populations is unavailable or (even when such information is available) when they are forced to rely on simplistic measures of welfare (e.g., household income or wealth). In this presentation, we explore algorithmic and computational challenges that arise in this settings.

In the first part of the talk, we explore one important dimension of economic welfare: susceptibility to income shocks in the form of an unexpected bill or disruption of one's income flow. We introduce and analyze a model of economic welfare that incorporates income, wealth, and external shocks and poses the question of how to allocate subsidies in this setting. We find that we can give optimal allocation mechanisms under natural assumptions on the agent's wealth and shock distributions, as well as approximation schemes in the general setting.

In the second part of the talk, we consider settings in which relevant information -- such as individuals' information needs -- is not available. Focusing on the lack of comprehensive, high-quality data about the health information needs of individuals in developing nations, we propose a bottom-up approach that uses search data from individuals in all 54 nations in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis; these searches reveal diverse health information needs that vary by demographic groups and geographic regions. We also shed light on discrepancies in the quality of content returned by search engines.

We conclude with a discussion on how algorithmic, computational, and mechanism design techniques can help inform interventions to improve access to opportunity in relevant domains and the Mechanism Design for Social Good research initiative.

This talk is based on joint work with Shawndra Hill, Jon Kleinberg, H. Andrew Schwartz, Peter M. Small, Jennifer Wortman Vaughan, and S. Matthew Weinberger.

Bio: Rediet Abebe is a Ph.D. candidate in computer science at Cornell University, advised by Professor Jon Kleinberg. Her research focuses on algorithms, AI, and applications to social good. She uses computational insights to improve access to opportunity, with a focus on under-served and marginalized communities. As part of this research mission, she co-founded and co-organizes the Mechanism Design for Social Good (MD4SG) initiative, an interdisciplinary, multi-institutional research group. She is also a co-founder and co-organizer of Black in AI, a transcontinental group aimed at increasing the presence and inclusion of Black researchers in the field of AI. Her research is deeply influenced by her upbringing in her hometown of Addis Ababa, Ethiopia, where she lived until moving to the U.S. in 2009. Her work has been generously supported by fellowships and scholarships through Facebook, Google, the Cornell Graduate School, and the Harvard-Cambridge Fellowship.