[AI Seminar] AI Seminar sponsored by Apple -- Rediet Abebe -- Dec. 11th

[Han Zhao]

Dear faculty and students:

We look forward to seeing you next Tuesday, Dec. 11th, at noon in *Univ.
Center, Danforth Conference Room *for AI Seminar sponsored by Apple. To
learn more about the seminar series, please visit the website.
On Tuesday, Rediet Abebe will give the following talk:

*Title: **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.
-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
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