Fairness in machine learning: a study of the Demographic Parity constraint
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Series
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SpeakersNicolas Schreuder (University of Genoa, Italy)
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FieldEconometrics
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LocationUniversity of Amsterdam, Room E5.22
Amsterdam -
Date and time
April 21, 2023
12:30 - 13:30
In various domains, statistical algorithms
trained on personal data take pivotal decisions which influence our lives on a
daily basis. Recent studies show that a naive use of these algorithms in
sensitive domains may lead to unfair and discriminating decisions, often
inheriting or even amplifying biases present in data. In the first part of the
talk, I will introduce and discuss the question of fairness in machine learning
through concrete examples of biases coming from the data and/or from the
algorithms. In a second part, I will demonstrate how statistical learning
theory can help us better understand and overcome some of those biases. In
particular, I will present a selection of recent results from two of my papers
on the Demographic Parity constraint:
A minimax framework for quantifying risk-fairness trade-off in regression (with
E. Chzhen), Ann. Statist. 50(4): 2416-2442 (Aug. 2022). DOI: 10.1214/22-AOS2198
Fair learning with Wasserstein barycenters for non-decomposable performance
measures (with S. Gaucher and E. Chzhen), AISTATS 2023.