Fairness: plurality, causality, and insurability-Reference-Cited by-同舟云学术

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Fairness: plurality, causality, and insurability

Published:2024-06-19 Issue:2 Volume:14 Page:317-328
ISSN:2190-9733
Container-title:European Actuarial Journal
language:en
Short-container-title:Eur. Actuar. J.

Author:

Fahrenwaldt Matthias,Furrer Christian^ORCID,Hiabu Munir Eberhardt,Huang Fei,Jørgensen Frederik Hytting,Lindholm Mathias,Loftus Joshua,Steffensen Mogens,Tsanakas Andreas

Abstract

AbstractThis article summarizes the main topics, findings, and avenues for future work from the workshop Fairness with a view towards insurance held August 2023 in Copenhagen, Denmark.

Funder

Fynske Købstæders Fond

Natur og Univers, Det Frie Forskningsråd

Copenhagen University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13385-024-00387-3.pdf

Reference39 articles.

1. Barocas S, Hardt M, Narayanan A (2023) Fairness and machine learning: limitations and opportunities. https://fairmlbook.org/pdf/fairmlbook.pdf. Accessed 21 May 2024

2. Barry L, Charpentier A (2020) Personalization as a promise: Can Big Data change the practice of insurance? Big Data Soc 7:1–12. https://doi.org/10.1177/2053951720935143

3. Bright L, Malinsky D, Thompson M (2016) Causally interpreting intersectionality theory. Philos Sci 83:60–81. https://doi.org/10.1086/684173

4. Charpentier A (2024) Insurance, biases, discrimination and fairness. Springer Actuarial. Springer, Cham (to appear)

5. Chen A, Vigna E (2017) A unisex stochastic mortality model to comply with EU Gender Directive. Insur Math Econ 73:124–136. https://doi.org/10.1016/j.insmatheco.2017.01.007

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