| 书目名称 | Insurance, Biases, Discrimination and Fairness | | 编辑 | Arthur Charpentier | | 视频video | | | 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 | | 丛书名称 | Springer Actuarial | | 图书封面 |  | | 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 | | 关键词 | Fairness; Predictive Models; Discrimination; Big Data; Actuarial Science; Insurance | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-49783-4 | | isbn_softcover | 978-3-031-49785-8 | | isbn_ebook | 978-3-031-49783-4Series ISSN 2523-3262 Series E-ISSN 2523-3270 | | issn_series | 2523-3262 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 18:49:21
