Fairness and discrimination in insurance

Tuesday, 27 January 2026

3.30 PM – 5 PM

Venue: Meeting Room 12 (#ABS-06-118) 

Chairperson: Asst Prof Jinggong Zhang

Abstract

What’s unique about insurance is that even statistical discrimination, which by definition is devoid of malicious intent, poses significant challenges. Because, on the one hand, policymakers would like insurers to treat their policyholders equally, without discrimination based on race, gender, age or other characteristics, even if it could make (statistical) sense to (indirectly) discriminate. On the other hand, at the core of actuaries’ activities lies discrimination, between risky and non-risky policyholders. And this risk is often statistically correlated with sensitive characteristics that regulation would like to prohibit insurers from taking into account. The analysis of possible discrimination in decision rules, whether human or algorithmic, is an old subject. Most of the concepts date back at least to the 50s, but recent developments in artificial intelligence have brought these issues back into the spotlight. Massive data facilitate statistical or proxy discrimination, and black-box algorithms do not facilitate understanding. Not to mention the various regulations that make it difficult to collect sensitive information, and ultimately test whether decisions can be discriminated against, especially indirectly. The talk is based on the textbook Insurance, Biases, Discrimination and Fairness, as well as recent papers, arXiv:2511.11294 (AAAI'26), arXiv:2408.03425 (AAAI'25), arXiv:2309.06627 (AAAI'24) and arXiv:2306.12912  (ECML'24).

About the Speaker

Author : Arthur Charpentier, PhD, is a Fellow of the French Institute of Actuaries and Full Professor of Mathematics at UQAM (Canada) and Université de Rennes (France). He is currently an invited professor at Kyoto University (Japan). Former Senior Editor of the Journal of Risk and Insurance, he is now Co-Editor of the European Actuarial Journal and serves on the ASTIN Bulletin editorial board. His research and teaching focus on predictive modeling in insurance, with emphasis on climate change, fairness, and discrimination. He earned his PhD from KU Leuven (2006).