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Introduction

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Insurance, Biases, Discrimination and Fairness

Part of the book series: Springer Actuarial ((SPACT))

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Abstract

Although the algorithms of machine-learning methods have brought issues of discrimination and fairness back to the forefront, these topics have been the subject of an extensive body of literature over the past decades. But dealing with discrimination in insurance is fundamentally an ill-defined, unsolvable problem. Nevertheless, we try to connect the dots, to explain different perspectives, going back to the legal, philosophical, and economic approaches to discrimination, before discussing the so-called concept of “actuarial fairness.” We offer some definitions, an overview of the book, as well as the datasets used in the illustrative examples throughout the chapters.

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Notes

  1. 1.

    See https://avalon.law.yale.edu/18th_century/rightsof.asp.

  2. 2.

    See https://www.moralmachine.net/.

  3. 3.

    Even if it seems exaggerated, because on the contrary, it is often humans who perform the repetitive tasks to help robots: “in most cases, the task is repetitive and mechanical. One worker explained that he once had to listen to recordings to find those containing the name of singer Taylor Swift in order to teach the algorithm that it is a person” as reported by Radio Canada in April 2019.

  4. 4.

    Member of the Chamber of Deputies from 1885 and 1893 and then Prime Minister of France from 1906 to 1909 and again from 1917 until 1920.

  5. 5.

    For simplicity, in most of the book, we discuss the case where S is a single sensitive attribute.

  6. 6.

    See Charpentier (2014) for a general overview on the use of R in actuarial science. Note that some packages mentioned here also exist in Python, in scikit-learn, as well as packages dedicated to fairness, such as fairlearn, or aif360).

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  1. Department of Mathematics, UQAM, Montreal, QC, Canada

    Arthur Charpentier

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Charpentier, A. (2024). Introduction. In: Insurance, Biases, Discrimination and Fairness. Springer Actuarial. Springer, Cham. https://doi.org/10.1007/978-3-031-49783-4_1

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