04380nam a22005895i 4500 978-3-031-49783-4 DE-He213 20251030112038.0 cr nn 008mamaa 240513s2024 sz | s |||| 0|eng d 9783031497834 978-3-031-49783-4 10.1007/978-3-031-49783-4 doi HG8779-8793 KFFN bicssc MAT003000 bisacsh KFFN thema Charpentier, Arthur. author. aut http://id.loc.gov/vocabulary/relators/aut 76717 Insurance, Biases, Discrimination and Fairness [electronic resource] / by Arthur Charpentier. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVIII, 483 p. 348 illus., 140 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Actuarial, 2523-3270 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. 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. Actuarial science. 76718 Social sciences Mathematics. 69736 Mathematical statistics. 67233 Statistics. 65982 Sampling (Statistics). 59805 Actuarial Mathematics. 76719 Mathematics in Business, Economics and Finance. 69739 Mathematical Statistics. 67233 Statistical Theory and Methods. Methodology of Data Collection and Processing. 65946 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031497827 Printed edition: 9783031497841 Printed edition: 9783031497858 Springer Actuarial, 2523-3270 76720 https://doi.org/10.1007/978-3-031-49783-4 ZDB-2-SMA ZDB-2-SXMS 59804 59804