7 papers
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…
Beyond Procedure: Substantive Fairness in Conformal Prediction
Pengqi Liu, Zijun Yu, Mouloud Belbahri +3
Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains und…
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,…
Functional Analysis of Loss-development Patterns in P&C Insurance
Arthur Charpentier, Qiheng Guo, Mike Ludkovski
We analyze loss development in NAIC Schedule P loss triangles using functional data analysis methods. Adopting the functional viewpoint, our dataset comprises 3300+ curves of incre…
Perceived Fairness in Networks
Arthur Charpentier
The usual definitions of algorithmic fairness focus on population-level statistics, such as demographic parity or equal opportunity. However, in many social or economic contexts, f…
When Numbers Mislead Us
Arthur Charpentier
The belief that numbers offer a single, objective description of reality overlooks a crucial truth: data does not speak for itself. Every dataset results from choices-what to measu…
KNN and K-means in Gini Prametric Spaces
Cassandra Mussard, Arthur Charpentier, Stéphane Mussard
This paper introduces enhancements to the K-means and K-nearest neighbors (KNN) algorithms based on the concept of Gini prametric spaces, instead of traditional metric spaces. Unli…