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VERSION:2.0
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CALSCALE:GREGORIAN
X-WR-CALNAME:Guest Speaker: Michael Colaresi\, University of Pittsburgh 
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260718T175941Z
UID:tag:localist.com\,2008:EventInstance_48747132701778
DTSTART:20250224T183000Z
DTEND:20250224T200000Z
DESCRIPTION:The School of Data Science and Society and the Department of Po
 litical Science in the College of Arts and Sciences are hosting a seminar 
 featuring Michael Colaresi\, associate vice provost for data science at th
 e University of Pittsburgh.\n\n \n\nWhy Models (and Not Just Data) Can Be 
 Biased and What that Means for Responsible Data Science and AI\nEverything
  from package delivery to missile targeting and from learning foreign lang
 uages to Nobel Prizes has been touched and in many cases transformed by th
 e availability of streaming data\, flexible algorithms and pervasive conne
 ctivity. Yet\, simultaneously\, there has been growing recognition that di
 gital tools can and often do have biases that distort information in ways 
 that harm individuals and groups. The goal of this lecture is to explore i
 n more detail the sources of biases in data-driven systems in order to imp
 rove downstream applications and decisions. There are many high profile ex
 amples in academic work\, policy white papers\, think tank position pieces
  and educational materials where bias in digital systems is attributed sim
 ply and solely to problems within the “input data”\, “training data
 ” or just the “data”. I argue that while misalignment of structured 
 inputs with the intentions of an application is indeed possible (and even 
 probable)\, models and algorithms themselves can and do also contribute to
  biased inferences and downstream harm. The presentation also offers a gen
 eral framework for clarifying how evidence and assumptions jointly produce
  inferences (good\, bad and ugly) and how an augmented Box’s Loop (Blei 
 2014) spotlights the pivotal role of data scientists in responsibly aligni
 ng machine performance with human principles. He will conclude with practi
 cal examples of their work improving this alignment in practical settings 
 from government\, industry and academia.\n\n \n\nAbout Michael Colaresi\nM
 ichael Colaresi is associate vice provost for data science and leads the R
 esponsible Data Science Initiative at the University of Pittsburgh. He is 
 the William S. Dietrich II Professor of Political Science\, assistant dire
 ctor of the Center for Research Computing\, and co-founding director of th
 e interdisciplinary computational social science major. His work leverages
  the accelerating availability of computational tools\, including machine 
 learning and Bayesian approaches\, along with unstructured information\, s
 uch as from digitized text\, to build and improve models of national secur
 ity problem-solving\, international and intrastate violence and changes in
  human rights over time. He also develops computational and visual tools t
 hat enable domain specialists to work alongside computer scientists to imp
 rove specific applications. He was previously the research and academic di
 rector of the Institute for Cyber Law\, Policy\, and Security\, co-editor 
 of the journal International Interactions and was co-recipient of the Best
  Visualization Award from the Journal of Peace Research in 2017 and the Go
 snell Prize for Excellence in Political Methodology from the Methodology s
 ection of the American Political Science Association in 2006. Colaresi’s
  research has been funded by four NSF grants and he is leading a large-sca
 le effort to develop scenario-based training in responsible data science t
 hat is funded by the Richard King Mellon Foundation. He is an external res
 earcher at the Peace Research Institute-Oslo and an external expert for th
 e European Commission.
GEO:35.903372;-79.048371
LOCATION:ITS Manning\, 2400
SUMMARY:Guest Speaker: Michael Colaresi\, University of Pittsburgh 
URL;VALUE=URI:https://calendar.unc.edu/event/guest-speaker-michael-colaresi
 -university-of-pittsburgh
CATEGORIES:Lectures
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