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Algorithms & Randomness Center (ARC)
Sampath Kannan
Monday, October 29, 2018
Klaus 1116 East – 11:00 am
Title: Fairness in Algorithmic Decision Making
Abstract: In this talk we survey some formulations of fairness requirements for decision making under uncertainty. We then discuss results from 3 recent papers:
1) Treating individuals fairly is not in conflict with long-term scientific learning goals if the population is sufficiently diverse.
2) When there is a pipeline of decisions, end-to-end fairness is impossible to achieve even in a very simple model.
3) Exploiting the knowledge acquired by others can unfairly advantage the free rider.
These papers are joint work with a number of co-authors:
Christopher Jung, Neil Lutz, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Steven Wu, and Juba Ziani
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