Insurance, Biases, Discrimination and Fairness / by Arthur Charpentier.

Format:

Book

Author/Creator:

Charpentier, Arthur, author.

Series:

Springer Actuarial, 2523-3270

Language:

English

Subjects (All):

Actuarial science.

Social sciences--Mathematics.

Social sciences.

Mathematical statistics.

Statistics.

Sampling (Statistics).

Actuarial Mathematics.

Mathematics in Business, Economics and Finance.

Mathematical Statistics.

Statistical Theory and Methods.

Methodology of Data Collection and Processing.

Local Subjects:

Actuarial Mathematics.

Mathematics in Business, Economics and Finance.

Mathematical Statistics.

Statistical Theory and Methods.

Methodology of Data Collection and Processing.

Physical Description:

1 online resource (491 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

Introduction

Part I Insurance and Predictive Modeling

Fundamentals of Actuarial Pricing

Models: Overview on Predictive Models

Models: Interpretability, Accuracy and Calibration

Part II Data

What Data?

Some Examples of Discrimination

Observations or Experiments: Data in Insurance

Part III Fairness

Group Fairness

Individual Fairness

Part IV Mitigation

Pre-processing

In-processing

Post-processing.

Notes:

Includes bibliographical references and index.

ISBN:

3-031-49783-X

OCLC:

1434171368