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   系統號碼955176
   書刊名Insurance, biases, discrimination and fairness [electronic resource] /
   主要著者Charpentier, Arthur.
   其他著者SpringerLink (Online service);臺灣學術電子書聯盟 (TAEBC)
   出版項Cham : Imprint: Springer, 2024.
   索書號HG8781
   ISBN9783031497834
   標題Insurance-Statistical methods.
Actuarial science.
Discrimination in insurance.
Sex discrimination in insurance.
Actuarial Mathematics.
Mathematics in Business, Economics and Finance.
   電子資源https://doi.org/10.1007/978-3-031-49783-4
   叢書名Springer actuarial,2523-3270;Springer actuarial.2523-3270
   
    
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內容簡介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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