5 citations · 15 across the 16 of their papers we have counts for
17 papers
Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation
Agathe Fernandes Machado, Charlotte Jamotton, Arthur Charpentier
Fairness audits with several sensitive attributes are often performed one coordinate at a time. This can miss purely intersectional disparities: a score may be independent of each…
Direct and Indirect Discrimination in Generalized Linear Models
Bertille Tierny, Arthur Charpentier, François Hu
Generalized linear models are central to actuarial modelling of binary risk, claim frequency, utilization, and cost-related outcomes. Yet fairness diagnostics often rely on linear-…
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…
Decomposing Probabilistic Scores: Reliability, Information Loss and Uncertainty
Arthur Charpentier, Agathe Fernandes Machado
Calibration is a conditional property that depends on the information retained by a predictor. We develop decomposition identities for arbitrary proper losses that make this depend…
Sequential Transport for Causal Mediation Analysis
Agathe Fernandes Machado, Iryna Voitsitska, Arthur Charpentier +1
We propose sequential transport (ST), a distributional framework for mediation analysis that combines optimal transport (OT) with a mediator directed acyclic graph (DAG). Instead o…
Linear Risk Sharing on Networks
Arthur Charpentier, Philipp Ratz
Over the past decade alternatives to traditional insurance and banking have grown in popularity. The desire to encourage local participation has lead products such as peer-to-peer…
Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models
Marouane Il Idrissi, Agathe Fernandes Machado, Arthur Charpentier
Cooperative game theory has become a cornerstone of post-hoc interpretability in machine learning, largely through the use of Shapley values. Yet, despite their widespread adoption…
Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals
Marouane Il Idrissi, Agathe Fernandes Machado, Ewen Gallic +1
Cooperative game theory methods, notably Shapley values, have significantly enhanced machine learning (ML) interpretability. However, existing explainable AI (XAI) frameworks mainl…
EquiPy: Sequential Fairness using Optimal Transport in Python
Agathe Fernandes Machado, Suzie Grondin, Philipp Ratz +2
Algorithmic fairness has received considerable attention due to the failures of various predictive AI systems that have been found to be unfairly biased against subgroups of the po…
Beyond Human Intervention: Algorithmic Collusion through Multi-Agent Learning Strategies
Suzie Grondin, Arthur Charpentier, Philipp Ratz
Collusion in market pricing is a concept associated with human actions to raise market prices through artificially limited supply. Recently, the idea of algorithmic collusion was p…
Optimal Transport on Categorical Data for Counterfactuals using Compositional Data and Dirichlet Transport
Agathe Fernandes Machado, Arthur Charpentier, Ewen Gallic
Recently, optimal transport-based approaches have gained attention for deriving counterfactuals, e.g., to quantify algorithmic discrimination. However, in the general multivariate…
Probabilistic Scores of Classifiers, Calibration is not Enough
Agathe Fernandes Machado, Arthur Charpentier, Emmanuel Flachaire +2
In binary classification tasks, accurate representation of probabilistic predictions is essential for various real-world applications such as predicting payment defaults or assessi…
Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
Agathe Fernandes Machado, Arthur Charpentier, Ewen Gallic
In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend "Knothe's rearrangement" and "triang…
From Uncertainty to Precision: Enhancing Binary Classifier Performance through Calibration
Agathe Fernandes Machado, Arthur Charpentier, Emmanuel Flachaire +2
The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model's…
Geospatial Disparities: A Case Study on Real Estate Prices in Paris
Agathe Fernandes Machado, François Hu, Philipp Ratz +2
Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contempo…
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…