Christophe Giraud
A Geometric Approach to Fair Online Learning
Abstract: Machine learning is ubiquitous in daily decisions and producing fair and non-discriminatory predictions is a major societal concern.
Various criteria of fairness have been proposed in the literature, and we will start with a (biased!) tour on fairness concepts in machine learning.
Many decision problems are of a sequential nature, and efforts are needed to better handle such settings.
We consider a general setting of fair online learning with stochastic sensitive and non-sensitive contexts.
We propose a unified approach for fair learning in this setting, by interpreting this problem as an approachability problem.
This point of view offers a generic way to produce algorithms and theoretical results.
Adapting Blackwell’s approachability theory, we exhibit a general necessary and sufficient
condition for some learning objectives to be compatible with some fairness constraints,
and we characterize the optimal trade-off between the two, when they are not compatible.
(joint work with E. Chzhen and G. Stoltz)
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