6 citations · 28 across the 12 of their papers we have counts for
12 papers
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…
Disaster Risk Financing through Taxation: A Framework for Regional Participation in Collective Risk-Sharing
Fallou Niakh, Arthur Charpentier, Caroline Hillairet +1
We consider an economy composed of different risk profile regions wishing to be hedged against a disaster risk using multi-region catastrophe insurance. Such catastrophic events in…
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…
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…
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…
Nonparametric Value-at-Risk via Sieve Estimation
Philipp Ratz
Artificial Neural Networks (ANN) have been employed for a range of modelling and prediction tasks using financial data. However, evidence on their predictive performance, especiall…
Collaborative Insurance Sustainability and Network Structure
Arthur Charpentier, Lariosse Kouakou, Matthias Löwe +2
The peer-to-peer (P2P) economy has been growing with the advent of the Internet, with well known brands such as Uber or Airbnb being examples thereof. In the insurance sector the a…