Insurance, Biases, Discrimination and Fairness
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...
Na minha lista:
| Autor principal: | |
|---|---|
| Autor Corporativo: | |
| Formato: | Recurso Electrónico livro electrónico |
| Idioma: | inglês |
| Publicado em: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
|
| Edição: | 1st ed. 2024. |
| Colecção: | Springer Actuarial,
|
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1007/978-3-031-49783-4 |
| Tags: |
Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-49783-4 | ||
| 003 | DE-He213 | ||
| 005 | 20260810165515.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240513s2024 sz | o |||| 0|eng d | ||
| 020 | |a 9783031497834 |9 978-3-031-49783-4 | ||
| 024 | 7 | |a 10.1007/978-3-031-49783-4 |2 doi | |
| 050 | 4 | |a HG8779-8793 | |
| 072 | 7 | |a KFFN |2 bicssc | |
| 072 | 7 | |a MAT003000 |2 bisacsh | |
| 072 | 7 | |a KFFN |2 thema | |
| 082 | 0 | 4 | |a 368.01 |2 23 |
| 100 | 1 | |a Charpentier, Arthur. |e author. |0 (orcid)0000-0003-3654-6286 |1 https://orcid.org/0000-0003-3654-6286 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Insurance, Biases, Discrimination and Fairness |h [electronic resource] / |c by Arthur Charpentier. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XVIII, 483 p. 348 illus.). |b 140 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background color |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Springer Actuarial, |x 2523-3270 | |
| 505 | 0 | |a 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. | |
| 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Actuarial science. | |
| 650 | 0 | |a Social sciences |x Mathematics. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Sampling (Statistics). | |
| 650 | 1 | 4 | |a Actuarial Mathematics. |
| 650 | 2 | 4 | |a Mathematics in Business, Economics and Finance. |
| 650 | 2 | 4 | |a Mathematical Statistics. |
| 650 | 2 | 4 | |a Statistical Theory and Methods. |
| 650 | 2 | 4 | |a Methodology of Data Collection and Processing. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031497827 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031497841 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031497858 |
| 830 | 0 | |a Springer Actuarial, |x 2523-3270 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-49783-4 |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||