Insurance, Biases, Discrimination and Fairness
Springer International Publishing (Verlag)
978-3-031-49782-7 (ISBN)
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.
lt;b>Arthur Charpentier, is an actuary (member of the International Actuarial Association), holds an MSc from ENSAE (Ecole Nationale de la Statistique, Paris, France) and a PhD in applied mathematics from KU Leuven (Belgium). After having been a professor in various institutions (mainly in France, ENSAE, Ecole Polytechnique, University of Rennes), he is now a professor at UQAM, Montreal. He has published several books in actuarial science and insurance modeling (including Mathematics of Non-Life Insurance, Computational Actuarial Science with R and more recently a Handbook of Insurance in French), as well as research and popularization articles, and is on the editorial board of some actuarial journals (ASTIN Bulletin, Risks, and the Journal of Risk and Insurance).
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.
Erscheinungsdatum | 16.05.2024 |
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Reihe/Serie | Springer Actuarial |
Zusatzinfo | XVIII, 483 p. 348 illus., 140 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
Wirtschaft | |
Schlagworte | actuarial science • Big Data • Discrimination • Fairness • insurance • predictive models |
ISBN-10 | 3-031-49782-1 / 3031497821 |
ISBN-13 | 978-3-031-49782-7 / 9783031497827 |
Zustand | Neuware |
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