7 papers
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…
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…
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…
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…
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…