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The Fairness of Machine Learning in Insurance: New Rags for an Old Man?

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  • Laurence Barry
  • Arthur Charpentier

Abstract

Since the beginning of their history, insurers have been known to use data to classify and price risks. As such, they were confronted early on with the problem of fairness and discrimination associated with data. This issue is becoming increasingly important with access to more granular and behavioural data, and is evolving to reflect current technologies and societal concerns. By looking into earlier debates on discrimination, we show that some algorithmic biases are a renewed version of older ones, while others show a reversal of the previous order. Paradoxically, while the insurance practice has not deeply changed nor are most of these biases new, the machine learning era still deeply shakes the conception of insurance fairness.

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  • Laurence Barry & Arthur Charpentier, 2022. "The Fairness of Machine Learning in Insurance: New Rags for an Old Man?," Papers 2205.08112, arXiv.org.
    • Handle: RePEc:arx:papers:2205.08112
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    References listed on IDEAS

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    Cited by:

    1. Arthur Charpentier, 2022. "Quantifying fairness and discrimination in predictive models," Papers 2212.09868, arXiv.org.

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