03986nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001600153072001700169072002300186072001600209100008200225245010000307250001800407264006900425300006900494336002600563337002600589338003600615347002400651490003500675505044700710520175001157650002302907650003402930650002902964650001602993650002703009650002703036650005203063650002903115650003603144650005103180710003403231773002603265776003603291776003603327776003603363830003503399856004603434978-3-031-49783-4DE-He21320251030112038.0cr nn 008mamaa240513s2024    sz |    s    |||| 0|eng d  a97830314978347 a10.1007/978-3-031-49783-42doi 4aHG8779-8793 7aKFFN2bicssc 7aMAT0030002bisacsh 7aKFFN2thema1 aCharpentier, Arthur.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aInsurance, Biases, Discrimination and Fairnessh[electronic resource] /cby Arthur Charpentier.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVIII, 483 p. 348 illus., 140 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringer Actuarial,x2523-32700 aIntroduction -- 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.  aThis 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. 0aActuarial science. 0aSocial sciencesxMathematics. 0aMathematical statistics. 0aStatistics. 0aSampling (Statistics).14aActuarial Mathematics.24aMathematics in Business, Economics and Finance.24aMathematical Statistics.24aStatistical Theory and Methods.24aMethodology of Data Collection and Processing.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303149782708iPrinted edition:z978303149784108iPrinted edition:z9783031497858 0aSpringer Actuarial,x2523-327040uhttps://doi.org/10.1007/978-3-031-49783-4