The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Insurance Discrimination and Fairness in Machine Learning: An Ethical Analysis
Abstract
Here we provide an ethical analysis of discrimination in private insurance to guide the application of non-discriminatory algorithms for risk prediction in the insurance context. This addresses the need for ethical guidance of data-science experts and business managers. The reference to private insurance as a business practice is essential in our approach, because the consequences of discrimination and predictive inaccuracy in underwriting are different from those of using predictive algorithms in other sectors (e.g. medical diagnosis, sentencing). Moreover, the computer science literature has demonstrated the existence of a trade-off in the extent to which one can pursue non- discrimination versus predictive accuracy. Again the moral assessment of this trade-off is related to the context of application.
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“They distinguish morally permissible and impermissible forms of statistical discrimination in private insurance and derive some ethical implications for the use of machine learning techniques in the insurance context.”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Loi and Christen (2019)
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“Loi and Christen (2019) provide an ethical analysis of private insurance discrimination and fairness in machine learning.”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Loi and Christen (2019)
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Cites
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
On the Currency of Egalitarian Justice
Cohen, G. A. (1989) · Ethics · doi:10.1086/293126 · Cited by 2,988
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159
What is Equality? Part 2: Equality of Resources
Dworkin, R. M. (2018) · Routledge eBooks · doi:10.4324/9781315199795-7 · Cited by 2,133
Equality and equal opportunity for welfare
Arneson, R. J. (1989) · Philosophical Studies · doi:10.1007/bf00646210 · Cited by 2,017
Cohen, G. A. (2008) · Harvard University Press eBooks · doi:10.4159/9780674029651 · Cited by 1,832
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827
Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098095 · Cited by 1,149
Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988
Research works printed but not linked: 9
- Andrew Altman, 2016, Discrimination, The Stanford Encyclopedia of Philosophy
- Ronen Avraham, 2014, Understanding Insurance Antidiscrimination Laws, S. Cal. L. Rev
- Reuben Binns, 2017, Fairness in Machine Learning: Lessons from Political Philosophy
- 10.1007/s10618-010-0190-x · DOI · Paper page
- Patrick Collinson, 2017, EU's Gender Ruling on Car Insurance Has Made Inequality Worse, The Guardian
- Norman Daniels, 2004, The Functions of Insurance and the Fairness of Genetic Underwriting, Genetics and Life Insurance: Medical Underwriting and Social Policy
- Roberta B Meyer, 2004, The Insurer Perspective, Genetics and Life Insurance: Medical Underwriting and Social Policy, 28
- Derek Parfit, 2003, Equality and Priority, Debates in Contemporary Political Philosophy: An Anthology, 115
- 2017, Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment, Cs, Stat, 1171
Cited by
The Discriminating (Pricing) Actuary
Frees, E. W. (., & Huang, F. (2023) · North American Actuarial Journal · doi:10.1080/10920277.2021.1951296 · Cited by 56
“For example, Canada passed federal laws in 2017 banning the use of all genetic information for business purposes; see, for example, Lombardo (2018). Within the United States, Born (2019) documented recent proposals in the state of Florida.”
Background
Metadata
- Authors
- Loi, M., & Christen, M.
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2019
- ISSN
- 1556-5068
- Cited by
- 3
- References
- 39
- Language
- en
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
