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DTSTART;TZID=America/New_York:20241117T140000
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UID:submissions.supercomputing.org_SC24_sess733_misc287@linklings.com
SUMMARY:The Unintended Effects of Privacy in Decision and Learning Talks
DESCRIPTION:Ferdinando Fioretto (University of Virginia)\n\nDifferential P
 rivacy has become the go-to approach for protecting sensitive information 
 in data releases and learning tasks that are used for critical decision pr
 ocesses. For example, census data is used to allocate funds and distribute
  benefits, while several corporations use machine learning systems for fin
 ancial predictions, hiring decisions, and more. While differential privacy
  provides strong guarantees, we will show that it may also induce biases a
 nd fairness issues in downstream decision processes. In this talk, we delv
 e into the intersection of privacy, fairness, and decision processes, with
  a focus on understanding and addressing these fairness issues. We first p
 rovide an overview of Differential Privacy and its applications in data re
 lease and learning tasks. Next, we examine the societal impacts of privacy
  through a fairness lens and present a framework to illustrate what aspect
 s of the private algorithms and/or data may be responsible for exacerbatin
 g unfairness. Finally, we propose a path to partially mitigate the observe
 d fairness issues and discus challenges that require further exploration.\
 n\nTag: Applications and Application Frameworks, Artificial Intelligence/M
 achine Learning, Security\n\nRegistration Category: Workshop Reg Pass\n\nS
 ession Chairs: nael abu-ghazaleh (University of California, Riverside); Ke
 vin J. Barker (Pacific Northwest National Laboratory (PNNL)); Yang Guo (Na
 tional Institute of Standards and Technology (NIST)); Joseph Manzano (Paci
 fic Northwest National Laboratory (PNNL)); Andres Marquez (Pacific Northwe
 st National Laboratory (PNNL)); and Sean Peisert (Lawrence Berkeley Nation
 al Laboratory (LBNL); University of California, Davis)\n\n
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