Description
Insurance, Biases, Discrimination and Fairness, 1st ed. 2024
Springer Actuarial Series
Author: Charpentier Arthur
Language: EnglishSubjects for Insurance, Biases, Discrimination and Fairness:
Keywords
Fairness; Predictive Models; Discrimination; Big Data; Actuarial Science; Insurance
485 p. · 15.5x23.5 cm · Hardback
Description
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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.
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