9783031497827 - Insurance, Biases, Discrimination and Fairness di Charpentier, Arthur (8 risultati)

Condizione: New. 2024th edition NO-PA16APR2015-KAP.

Buch. Condizione: 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.

Buch. Condizione: 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.

Condizione: 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.

Buch. Condizione: Neu. Insurance, Biases, Discrimination and Fairness | Arthur Charpentier | Buch | xviii | Englisch | 2024 | Springer | EAN 9783031497827 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Condizione: New. Print on Demand.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.

Condizione: New. PRINT ON DEMAND.