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Insurance, biases, discrimination and fairness

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Insurance, biases, discrimination and fairness/ by Arthur Charpentier.
作者:	Charpentier, Arthur.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xviii, 483 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Insurance - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-031-49783-4
ISBN:	9783031497834

Insurance, biases, discrimination and fairness
Charpentier, Arthur.

Insurance, biases, discrimination and fairness[electronic resource] /by Arthur Charpentier. - Cham :Springer Nature Switzerland :2024. - xviii, 483 p. :ill. (some col.), digital ;24 cm. - Springer actuarial,2523-3270. - Springer actuarial..

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.

ISBN: 9783031497834

Standard No.: 10.1007/978-3-031-49783-4doiSubjects--Topical Terms:

716533
Insurance
--Statistical methods.

LC Class. No.: HG8781

Dewey Class. No.: 368.01

Insurance, biases, discrimination and fairness
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520 $a 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.
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