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Cited by 3

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

Loi, Michele, Christen, Markus·2019·SSRN Electronic Journal
DOI 10.2139/ssrn.3438823
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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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Fig. 1 Causal links responsible for correlations between sunscreen purchases and life insurance in Ex.3.bis
Fig. 1 Causal links responsible for correlations between sunscreen purchases and life insurance in Ex.3.bisCC-BY · From the PDF

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Citations

Cites

Research works printed but not linked: 9

  1. Andrew Altman, 2016, Discrimination, The Stanford Encyclopedia of Philosophy
  2. Ronen Avraham, 2014, Understanding Insurance Antidiscrimination Laws, S. Cal. L. Rev
  3. Reuben Binns, 2017, Fairness in Machine Learning: Lessons from Political Philosophy
  4. 10.1007/s10618-010-0190-x · DOI · Paper page
  5. Patrick Collinson, 2017, EU's Gender Ruling on Car Insurance Has Made Inequality Worse, The Guardian
  6. Norman Daniels, 2004, The Functions of Insurance and the Fairness of Genetic Underwriting, Genetics and Life Insurance: Medical Underwriting and Social Policy
  7. Roberta B Meyer, 2004, The Insurer Perspective, Genetics and Life Insurance: Medical Underwriting and Social Policy, 28
  8. Derek Parfit, 2003, Equality and Priority, Debates in Contemporary Political Philosophy: An Anthology, 115
  9. 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

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Metadata

Authors
Loi, M., & Christen, M.
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2019
ISSN
1556-5068
DOI
10.2139/ssrn.3438823
Cited by
3
References
39
Language
en

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