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DTSTAMP:20191022T231806Z
DTSTART;TZID=America/Los_Angeles:20191104T153000
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TRANSP:OPAQUE
SUMMARY:Anil Aswani — Optimization Hierarchy for Fair Statistical Decision Problems
UID:129279-ucb-events-calendar@berkeley.edu
ORGANIZER;CN="UC Berkeley Calendar Network":
LOCATION:1174 Etcheverry Hall
DESCRIPTION:Anil Aswani\, University of California\, Berkeley\n\nAbstract: Data-driven decision-making has drawn scrutiny from policy makers due to fears of potential discrimination\, and a growing literature has begun to develop fair statistical techniques. However\, these techniques are often specialized to one model context and based on ad-hoc arguments\, which makes it difficult to perform theoretical analysis. This paper develops an optimization hierarchy for fair statistical decision problems. Because our hierarchy is based on the framework of statistical decision problems\, this means it provides a systematic approach for developing and studying fair versions of hypothesis testing\, decision-making\, estimation\, regression\, and classification. We use the insight that qualitative definitions of fairness are equivalent to statistical independence between the output of a statistical technique and a random variable that measures attributes for which fairness is desired. We use this insight to construct an optimization hierarchy that lends itself to numerical computation\, and we use tools from variational analysis and random set theory to prove that higher levels of this hierarchy lead to consistency in the sense that it asymptotically imposes this independence as a constraint in corresponding statistical decision problems. We demonstrate numerical effectiveness of our hierarchy using several data sets\, and we conclude by using our hierarchy to fairly perform automated dosing of morphine.\n\nThis talk is based upon the preprint at https://arxiv.org/abs/1910.08520.
URL:http://events.berkeley.edu/index.php/calendar/sn/pubaff.html?event_ID=129279&view=preview
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