Articoli correlati a Insurance, Biases, Discrimination and Fairness
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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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
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).
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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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreSpringer Nature
- Data di pubblicazione2024
- ISBN 10 3031497821
- ISBN 13 9783031497827
- RilegaturaCopertina rigida
- LinguaInglese
- Numero di pagine504
- Contatto del produttoreSpringer Nature Customer Service Center GmbH; ProductSafety@springernature.com
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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. Codice articolo 1228909175
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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. Codice articolo 9783031497827
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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. Codice articolo 9783031497827
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Buch. Condizione: 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. Codice articolo 9783031497827
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