Insurance, Biases, Discrimination and Fairness(Springer Actuarial)

保险、偏见、歧视与公平

数学史

售   价:
1326.00
发货周期:国外库房发货,通常付款后3-5周到货!
作      者
出  版 社
出版时间
2024年02月11日
装      帧
精装
ISBN
9783031497827
复制
页      码
470
语      种
英文
综合评分
暂无评分
我 要 买
- +
库存 30 本
  • 图书详情
  • 目次
  • 买家须知
  • 书评(0)
  • 权威书评(0)
图书简介
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.
本书暂无推荐
本书暂无推荐
看了又看
  • 上一个
  • 下一个