<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class=""><div dir="auto" class="" style="word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><font color="#831100" class=""><span class="" style="font-size: 14px;"><b class="">University of Chicago and Toyota Technological Institute at Chicago<br class="">Machine Learning Seminar Series</b></span></font><br class=""><br class=""><br class=""><b class="" style="font-size: 14px;">Mesrob Ohannessian</b><br class=""><span class="" style="font-size: 14px;">TTIC</span><br class=""><br class=""><b class="" style="font-size: 14px;">Wednesday, April 10, 2019 at 1:00 pm<br class="">Harper Center (Booth) Room C25</b><br class=""><br class=""><br class=""><b class="" style="font-size: 14px;">Title:</b></font></div><div dir="auto" class="" style="word-wrap: break-word; line-break: after-white-space;"><font class="" face="Arial" style="font-size: 14px;">From Fair Decisions to Social Benefit</font><font face="Arial" class="" style="font-size: 14px;"> <br class=""><b class=""><font face="Arial" class="" style="font-weight: normal;"><b class=""><br class=""></b></font></b></font></div><div dir="auto" class="" style="word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><span class=""><font face="Arial" class="" style="font-size: 14px;"><b class="">Abstract:</b><br class=""></font><div class=""><span class="" style="font-size: 14px;">Using data efficiently is no longer our only burden. As automated decisions in socio-technical systems affect more lives than ever, it is imperative that we also use data responsibly. Being non-discriminatory and fair is one such responsibility. To date, explorations of fairness in intelligent decision systems have mostly ignored long-term influence on the underlying population. In this talk I give a first comprehensive perspective of this problem. Among the various insights I provide is quantifying both sides of the mismatch hypothesis: when can we hope that affirmative action is indeed beneficial to society?</span></div><font face="Arial" class="" style="font-size: 14px;"><br class=""><b class="">Bio:</b><br class=""></font><div class=""><span class="" style="font-size: 14px;">Mesrob I. Ohannessian is a Research Assistant Professor at the Toyota Technological Institute at Chicago. He was previously a postdoc at UCSD, MSR-Inria, and Université Paris-Sud. He received his PhD in EECS from MIT. His research interests are in machine learning, statistics, information theory, and their applications, particularly to problems marked by data scarcity and to decisions that affect society.</span></div><font face="Arial" class="" style="font-size: 14px;"><br class=""><br class=""><b class=""><i class="">Host: </i></b></font></span></font><font face="Arial" class=""><span class="" style="font-size: 14px;"><b class=""><i class="">Rebecca Willett </i></b></span></font></div><div dir="auto" class="" style="word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><span class="" style="font-size: 14px;"><b class=""><i class=""><br class=""></i></b></span></font></div><div dir="auto" class="" style="word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><span class="" style="font-size: 14px;"><b class=""><i class=""><br class=""></i></b></span></font></div><div dir="auto" class="" style="word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><span class="" style="font-size: 14px;"><b class=""><i class=""></i></b></span></font></div></div></body></html>