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020 _a9783031497834
_9978-3-031-49783-4
024 7 _a10.1007/978-3-031-49783-4
_2doi
050 4 _aHG8779-8793
072 7 _aKFFN
_2bicssc
072 7 _aMAT003000
_2bisacsh
072 7 _aKFFN
_2thema
100 1 _aCharpentier, Arthur.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976717
245 1 0 _aInsurance, Biases, Discrimination and Fairness
_h[electronic resource] /
_cby Arthur Charpentier.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVIII, 483 p. 348 illus., 140 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Actuarial,
_x2523-3270
505 0 _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.
520 _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.
650 0 _aActuarial science.
_976718
650 0 _aSocial sciences
_xMathematics.
_969736
650 0 _aMathematical statistics.
_967233
650 0 _aStatistics.
_965982
650 0 _aSampling (Statistics).
_959805
650 1 4 _aActuarial Mathematics.
_976719
650 2 4 _aMathematics in Business, Economics and Finance.
_969739
650 2 4 _aMathematical Statistics.
_967233
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aMethodology of Data Collection and Processing.
_965946
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031497827
776 0 8 _iPrinted edition:
_z9783031497841
776 0 8 _iPrinted edition:
_z9783031497858
830 0 _aSpringer Actuarial,
_x2523-3270
_976720
856 4 0 _uhttps://doi.org/10.1007/978-3-031-49783-4
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c59804
_d59804