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Titlebook: Insurance, Biases, Discrimination and Fairness; Arthur Charpentier Book 2024 The Editor(s) (if applicable) and The Author(s), under exclus

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發(fā)表于 2025-3-21 18:49:21 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Insurance, Biases, Discrimination and Fairness
編輯Arthur Charpentier
視頻videohttp://file.papertrans.cn/469/468201/468201.mp4
概述An account of fairness in predictive models.Discusses fairness issues arising from big data and algorithms.Addresses a topic of high interest to actuaries and regulators
叢書名稱Springer Actuarial
圖書封面Titlebook: Insurance, Biases, Discrimination and Fairness;  Arthur Charpentier Book 2024 The Editor(s) (if applicable) and The Author(s), under exclus
描述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 "mach
出版日期Book 2024
關(guān)鍵詞Fairness; Predictive Models; Discrimination; Big Data; Actuarial Science; Insurance
版次1
doihttps://doi.org/10.1007/978-3-031-49783-4
isbn_softcover978-3-031-49785-8
isbn_ebook978-3-031-49783-4Series ISSN 2523-3262 Series E-ISSN 2523-3270
issn_series 2523-3262
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:59:56 | 只看該作者
Models: Overview on Predictive Modelsf classes, and then to modern insurance, with the concept of “premium personalization.” Modern modeling techniques are presented, starting with econometric approaches, before presenting machine-learning techniques.
板凳
發(fā)表于 2025-3-22 00:48:32 | 只看該作者
Observations or Experiments: Data in Insuranceand causality, we describe the “causation ladder,” and the three rungs: association or correlation (“.”), intervention (“.”), and counterfactuals (“.”). Counterfactuals are important for quantifying discrimination.
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In-processinghe model is required to satisfy constraints, a natural idea is to add a penalty term in the objective function. The idea of “in-processing” is to get a trade-off between accuracy and fairness. As previously, we present that approach to some datasets.
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978-3-031-49785-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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