30 citations · 74 across the 20 of their papers we have counts for
4 papers · 1 filter
Quantifying fairness and discrimination in predictive models
Arthur Charpentier
The analysis of discrimination has long interested economists and lawyers. In recent years, the literature in computer science and machine learning has become interested in the sub…
Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach
Menna Hassan, Nourhan Sakr, Arthur Charpentier
This paper designs a sequential repeated game of a micro-founded society with three types of agents: individuals, insurers, and a government. Nascent to economics literature, we us…
The Fairness of Machine Learning in Insurance: New Rags for an Old Man?
Laurence Barry, Arthur Charpentier
Since the beginning of their history, insurers have been known to use data to classify and price risks. As such, they were confronted early on with the problem of fairness and disc…
A Fair Pricing Model via Adversarial Learning
Vincent Grari, Arthur Charpentier, Marcin Detyniecki
At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher p…