6 citations · 28 across the 12 of their papers we have counts for
5 papers · 1 filter
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 Explainability using Optimal Transport with Applications in Image Classification
Philipp Ratz, François Hu, Arthur Charpentier
Ensuring trust and accountability in Artificial Intelligence systems demands explainability of its outcomes. Despite significant progress in Explainable AI, human biases still tain…
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…