Name: Insurance, Biases, Discrimination and Fairness
ISBN: 978-3-031-49782-7

Overview

Authors:

Arthur Charpentier ⁰

Arthur Charpentier
1. Department of Mathematics, UQAM, Montreal, Canada
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An account of fairness in predictive models
Discusses fairness issues arising from big data and algorithms
Addresses a topic of high interest to actuaries and regulators

Part of the book series: Springer Actuarial (SPACT)

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Keywords

Fairness
Predictive Models
Discrimination
Big Data
Actuarial Science
Insurance

About this book

This book offers an introduction to the technical foundations of discrimination and equity issues in insurance models, catering to undergraduates, postgraduates, and practitioners. It is a self-contained resource, accessible to those with a basic understanding of probability and statistics. Designed as both a reference guide and a means to develop fairer models, the book acknowledges the complexity and ambiguity surrounding the question of discrimination in insurance. In insurance, proposing differentiated premiums that accurately reflect policyholders' true risk—termed "actuarial fairness" or "legitimate discrimination"—is economically and ethically motivated. However, such segmentation can appear discriminatory from a legal perspective. By intertwining real-life examples with academic models, the book incorporates diverse perspectives from philosophy, social sciences, economics, mathematics, and computer science. Although discrimination has long been a subject of inquiry in economics and philosophy, it has gained renewed prominence in the context of "big data," with an abundance of proxy variables capturing sensitive attributes, and "artificial intelligence" or specifically "machine learning" techniques, which often involve less interpretable black box algorithms.

The book distinguishes between models and data to enhance our comprehension of why a model may appear unfair. It reminds us that while a model may not be inherently good or bad, it is never neutral and often represents a formalization of a world seen through potentially biased data. Furthermore, the book equips actuaries with technical tools to quantify and mitigate potential discrimination, featuring dedicated chapters that delve into these methods.

Authors and Affiliations

Department of Mathematics, UQAM, Montreal, Canada

Arthur Charpentier

About the author

Arthur Charpentier, is an actuary (member of the International Actuarial Association), holds an MSc from ENSAE (Ecole Nationale de la Statistique, Paris, France) and a PhD in applied mathematics from KU Leuven (Belgium). After having been a professor in various institutions (mainly in France, ENSAE, Ecole Polytechnique, University of Rennes), he is now a professor at UQAM, Montreal. He has published several books in actuarial science and insurance modeling (including Mathematics of Non-Life Insurance, Computational Actuarial Science with R and more recently a Handbook of Insurance in French), as well as research and popularization articles, and is on the editorial board of some actuarial journals (ASTIN Bulletin, Risks, and the Journal of Risk and Insurance).

Bibliographic Information

Book Title: Insurance, Biases, Discrimination and Fairness
Authors: Arthur Charpentier
Series Title: Springer Actuarial
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-49782-7Due: 10 June 2024
Softcover ISBN: 978-3-031-49785-8Due: 10 June 2024
eBook ISBN: 978-3-031-49783-4Due: 10 June 2024
Series ISSN: 2523-3262
Series E-ISSN: 2523-3270
Edition Number: 1
Number of Pages: XVIII, 485

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Insurance, Biases, Discrimination and Fairness