Insurance, Biases, Discrimination and Fairness, 1st ed. 2024 Springer Actuarial Series
Auteur : Charpentier Arthur
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
Date de parution : 04-2024
Ouvrage de 485 p.
15.5x23.5 cm
Thèmes d’Insurance, Biases, Discrimination and Fairness :
Mots-clés :
Fairness; Predictive Models; Discrimination; Big Data; Actuarial Science; Insurance