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Mathematical Sciences Research Institute

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Program

Algorithms, Fairness, and Equity August 21, 2023 to December 20, 2023
Organizers Vincent Conitzer (Duke University), Moon Duchin (Tufts University), Bettina Klaus (Université de Lausanne), Jonathan Mattingly (Duke University), LEAD Wesley Pegden (Carnegie Mellon University)
Description
Image
A graphical representation of a Markov Chain fairness analysis of a political districting in North Carolina from Chin, Herschlag, Mattingly
This program aims to bring together researchers working at the interface of fairness and computation. This interface has been the site of intensive research effort in mechanism design, in research on partitioning problems related to political districting problems, and in research on ways to address issues of fairness and equity in the context of machine learning algorithms. These areas each approach the relationship between mathematics and fairness from a distinct perspective. In mechanism design, algorithms are a tool to achieve outcomes with mathematical guarantees of various notions of fairness. In machine learning, we perceive failures of fairness as an undesirable side effect of learning approaches, and seek mathematical approaches to understand and mitigate these failures. And in partitioning problems like political districting, we often seek mathematical tools to evaluate the fairness of human decisions. This program will explore progress in these areas while also providing a venue for overlapping perspectives. The topics workshop “Randomization, neutrality, and fairness” will explore the common role randomness and probability has played in these lines of work.
Keywords and Mathematics Subject Classification (MSC)
Tags/Keywords
  • Fairness

  • machine learning

  • mechanism design

  • game theory

  • Markov chains

  • randomness

  • Gerrymandering

Primary Mathematics Subject Classification
Secondary Mathematics Subject Classification No Secondary AMS MSC
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