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What Data?

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Insurance, Biases, Discrimination and Fairness

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

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Abstract

Actuaries now collect all kinds of information about policyholders, which can not only be used to refine a premium calculation but also to carry out prevention operations. We return here to the choice of relevant variables in pricing, with emphasis on actuarial, operational, legal and ethical motivations. In particular, we discuss the idea of capturing information on the behavior of an insured person, and the difficult reconciliation with the strong constraints not only of privacy but also of fairness.

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Notes

  1. 1.

    Euclid’s treatise on plane geometry was named δεδομένα , translated as “data.”

  2. 2.

    CNIL is the Commission Nationale de l’Informatique et des Libertés (National Commission on Informatics and Liberty), an independent French administrative regulatory body whose mission is to ensure that data privacy law is applied to the collection, storage, and use of personal data, in France.

  3. 3.

    See https://gdpr-info.eu/.

  4. 4.

    See https://www.coe.int/en/web/data-protection/convention108-and-protocol.

  5. 5.

    CA: California, HI: Hawaii, GA: Georgia, NC: North Carolina, NY: New York, MA: Massachusetts, PA: Pennsylvania, FL: Florida, TX: Texas.

  6. 6.

    AL: Alberta, ON: Ontario, NB: New Brunswick, NL: Newfoundland and Labrador, QC: Québec.

  7. 7.

    Project https://immersion.media.mit.edu/.

  8. 8.

    Instead of the Latin formula that could designate a contract. Actually, “formula” nowadays refers to “mathematical formulas” as seen in Chaps. 3 and 4, or “magic formulas,” the two being very close for many people (see for example the introduction of O’Neil (2016) explaining how mathematics “was not only deeply entangled in the world’s problems but also fueling many of them. The housing crisis, the collapse of major financial institutions, the rise of unemployment—all had been aided and abetted by mathematicians wielding magic formulas.”

  9. 9.

    See https://vimeo.com/123722811.

  10. 10.

    Equivalent, in the UK, of 911 in North America, 112 in many European countries, or 0118 999 881 999 119 725 3.

  11. 11.

    To continue the analogy, in credit risk, we find the three previous levels, with (1) those who do not apply for credit, (2) those to whom the institution does not offer credit, and (3) those who are not interested in the offer made.

  12. 12.

    Called “The Great AI Debate: Interpretability is necessary for machine learning,” opposing Rich Caruana and Patrice Simard (for) to Kilian Weinberger and Yann LeCun (against) https://youtu.be/93Xv8vJ2acI.

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

    Arthur Charpentier

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Charpentier, A. (2024). What Data?. In: Insurance, Biases, Discrimination and Fairness. Springer Actuarial. Springer, Cham. https://doi.org/10.1007/978-3-031-49783-4_5

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