6 citations · 25 across the 11 of their papers we have counts for
7 papers · 1 filter
Fair regression under localized demographic parity constraints
Arthur Charpentier, Christophe Denis, Romuald Elie +2
Demographic parity (DP) is a widely used group fairness criterion requiring predictive distributions to be invariant across sensitive groups. While natural in classification, full…
Federated Measurement of Demographic Disparities from Quantile Sketches
Arthur Charpentier, Agathe Fernandes Machado, Olivier Côté +1
Many fairness goals are defined at a population level that misaligns with siloed data collection, which remains unsharable due to privacy regulations. Horizontal federated learning…
Decomposing Direct and Indirect Biases in Linear Models under Demographic Parity Constraint
Bertille Tierny, Arthur Charpentier, François Hu
Linear models are widely used in high-stakes decision-making due to their simplicity and interpretability. Yet when fairness constraints such as demographic parity are introduced,…
Parametric Fairness with Statistical Guarantees
François HU, Philipp Ratz, Arthur Charpentier
Algorithmic fairness has gained prominence due to societal and regulatory concerns about biases in Machine Learning models. Common group fairness metrics like Equalized Odds for cl…
A Sequentially Fair Mechanism for Multiple Sensitive Attributes
François Hu, Philipp Ratz, Arthur Charpentier
In the standard use case of Algorithmic Fairness, the goal is to eliminate the relationship between a sensitive variable and a corresponding score. Throughout recent years, the sci…
Fairness in Multi-Task Learning via Wasserstein Barycenters
François Hu, Philipp Ratz, Arthur Charpentier
Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data. Recent advances have proposed various methods to ensure fairness in a univariat…
Mitigating Discrimination in Insurance with Wasserstein Barycenters
Arthur Charpentier, François Hu, Philipp Ratz
The insurance industry is heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long…