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Insurance, Biases, Discrimination and Fairness

  • Book
  • © 2024

Overview

Authors:
  1. 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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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.

Keywords

Table of contents (12 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Introduction

    • Arthur Charpentier
    Pages 1-22

  3. Insurance and Predictive Modeling

    1. Front Matter

      Pages 23-23
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    2. Fundamentals of Actuarial Pricing

      • Arthur Charpentier
      Pages 25-57

    3. Models: Overview on Predictive Models

      • Arthur Charpentier
      Pages 59-122

    4. Models: Interpretability, Accuracy, and Calibration

      • Arthur Charpentier
      Pages 123-176

  4. Data

    1. Front Matter

      Pages 177-177
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    2. What Data?

      • Arthur Charpentier
      Pages 179-216

    3. Some Examples of Discrimination

      • Arthur Charpentier
      Pages 217-273

    4. Observations or Experiments: Data in Insurance

      • Arthur Charpentier
      Pages 275-305

  5. Fairness

    1. Front Matter

      Pages 307-307
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    2. Group Fairness

      • Arthur Charpentier
      Pages 309-355

    3. Individual Fairness

      • Arthur Charpentier
      Pages 357-382

  6. Mitigation

    1. Front Matter

      Pages 383-383
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    2. Pre-processing

      • Arthur Charpentier
      Pages 385-396

    3. In-processing

      • Arthur Charpentier
      Pages 397-415

    4. Post-Processing

      • Arthur Charpentier
      Pages 417-433

  7. Back Matter

    Pages 435-483
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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).

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