Articles liés à Insurance, Biases, Discrimination and Fairness
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Arthur Charpentier, is an actuary (member of the International Actuarial Association), holds an MSc from ENSAE (Ecole Nationale de la Statistique, Paris, France) and a PhD in applied mathematics from KU Leuven (Belgium). After having been a professor in various institutions (mainly in France, ENSAE, Ecole Polytechnique, University of Rennes), he is now a professor at UQAM, Montreal. He has published several books in actuarial science and insurance modeling (including Mathematics of Non-Life Insurance, Computational Actuarial Science with R and more recently a Handbook of Insurance in French), as well as research and popularization articles, and is on the editorial board of some actuarial journals (ASTIN Bulletin, Risks, and the Journal of Risk and Insurance).
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurSpringer International Publishing AG
- Date d'édition2024
- ISBN 10 3031497821
- ISBN 13 9783031497827
- ReliureRelié
- Langueanglais
- Nombre de pages483
- Coordonnées du fabricantnon disponible
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Insurance, Biases, Discrimination and Fairness
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Springer Nature Switzerland, 2024
ISBN 10 : 3031497821
ISBN 13 : 9783031497827
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. An account of fairness in predictive modelsDiscusses fairness issues arising from big data and algorithmsAddresses a topic of high interest to actuaries and regulatorsArthur Charpentier, is an actuary (member of the Internati. N° de réf. du vendeur 1228909175
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Insurance, Biases, Discrimination and Fairness
Edité par
Springer Nature Switzerland, 2024
ISBN 10 : 3031497821
ISBN 13 : 9783031497827
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783031497827
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Insurance, Biases, Discrimination and Fairness
ISBN 10 : 3031497821
ISBN 13 : 9783031497827
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 483 pp. Englisch. N° de réf. du vendeur 9783031497827
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Insurance, Biases, Discrimination and Fairness
ISBN 10 : 3031497821
ISBN 13 : 9783031497827
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 504 pp. Englisch. N° de réf. du vendeur 9783031497827
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