Research map / Learning theory and algorithms / Fairness

Empirical Likelihood for Fair Classification

ICLR 2024 · Learning theory and algorithms › Fairness · approach: Causal and policy fairness in decisions

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Proposes an empirical likelihood based approach to fair classification that enforces fairness constraints in a classifier training objective.

Problem

Machine learning classifiers trained to minimize error may discriminate against individuals based on sensitive attributes such as gender, race, or age.

Approach

Uses empirical likelihood to formulate and enforce fairness constraints for classification; the abstract is truncated so the specific formulation is not stated.

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