TY  - BOOK
AU  - Charpentier,Arthur
ED  - SpringerLink (Online service)
TI  - Insurance, Biases, Discrimination and Fairness
T2  - Springer Actuarial,
SN  - 9783031497834
AV  - HG8779-8793 
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Actuarial science
KW  - Social sciences
KW  - Mathematics
KW  - Mathematical statistics
KW  - Statistics
KW  - Sampling (Statistics)
KW  - Actuarial Mathematics
KW  - Mathematics in Business, Economics and Finance
KW  - Mathematical Statistics
KW  - Statistical Theory and Methods
KW  - Methodology of Data Collection and Processing
N1  - Introduction -- Part I Insurance and Predictive Modeling -- Fundamentals of Actuarial Pricing -- Models: Overview on Predictive Models -- Models: Interpretability, Accuracy and Calibration -- Part II Data -- What Data? -- Some Examples of Discrimination -- Observations or Experiments: Data in Insurance -- Part III Fairness -- Group Fairness -- Individual Fairness -- Part IV Mitigation -- Pre-processing -- In-processing -- Post-processing
N2  - 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
UR  - https://doi.org/10.1007/978-3-031-49783-4
ER  -