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Insurance, Big Data and Changing Conceptions of Fairness

Published online by Cambridge University Press:  06 July 2020

Laurence Barry
Laurence Barry*
Affiliation:
chaire PARI (ENSAE/Sciences Po), Paris, France [Laurence.Barry@datastorm.fr]
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Abstract

This paper aims to show how insurance mechanisms that historically propelled a conception of fairness based on solidarity and a collective approach shifted along the 20th century towards an idealistic adjustment to individual risk. Insurance originally assumed that, while individual hazards remained unknown, risk could be measured and managed on the aggregate. An examination of the proceedings of the American Casualty Actuarial Society (CAS) during the 20th century demonstrates the slow crystallization of another conception of fairness, that aims at a scientific adjustment of insurance premiums to actual “individual risks.” I argue that this conception of fairness deconstructs the one based on solidarity. Big data technologies have further radicalized this shift. By aiming at predictive individual risk scores rather than average costs estimated on the aggregate, the algorithms contribute to replacing fairness as solidarity by the correctness of a computation.

Résumé

Résumé

Cet article montre comment les mécanismes d’assurance qui ont historiquement favorisé une conception de l’équité fondée sur la solidarité et une approche collective se sont déplacés au cours du xxe siècle vers un ajustement idéalisé autour du risque individuel. À l’origine, les assurances partaient du principe que si les aléas individuels restaient inconnus, le risque pouvait être mesuré et géré globalement. L’examen des travaux de l’American Casualty Actuarial Society (CAS) au cours du xxe siècle met en évidence la lente cristallisation d’une autre conception de l’équité, l’équité actuarielle, qui recherche un ajustement scientifique des primes d’assurance autour des “risques individuels” réels. Je considère que cette conception de l’équité déconstruit celle fondée sur la solidarité. Les technologies de données massives contribuent toujours plus à radicaliser ce changement. En produisant des scores de risque individuels prédictifs plutôt que des coûts moyens estimés sur une totalité, les algorithmes contribuent à remplacer l’équité comme solidarité par la justesse d’un calcul.

Zusammenfassung

Zusammenfassung

Dieser Artikel zeigt, wie sich Versicherungsmechanismen, deren Ursprünge auf eine ausgleichende Gerechtigkeit mit solidarischem und kollektivem Ansatz zurückgehen, im Laufe des 20. Jahrhunderts zu einer idealisierten Anpassung an individuelle Risiken weiterentwickelt haben. Ursprünglich galt für Versicherungen folgende Prämisse: selbst wenn individuelle Gefahren unbekannt bleiben, kann das Risiko global gemessen und gehandhabt werden. Ein Rückblick auf die Arbeit der American Casualty Actuarial Society (CAS) während des 20. Jahrhunderts verdeutlicht die langsame Herauskristallisierung einer anderen Definition von ausgleichender Gerechtigkeit, und zwar der versicherungsmathematischen Gerechtigkeit, mit dem erklärten Ziel einer wissenschaftlichen Anpassung der Versicherungsprämien an reale „individuellen Risiken“. Ich bin der Meinung, dass diese Vorstellung von Gerechtigkeit die auf Solidarität beruhende dekonstruiert. Massive Datentechnologien tragen zunehmend dazu bei, diesen Wandel zu radikalisieren. Wenn prädiktive individuelle Risiko-Scores anstatt geschätzter Durchschnittskosten einer Gesamtgröße genutzt werden, führen Algorithmen dazu, dass die solidarisch getragene Gerechtigkeit durch die Richtigkeit einer Berechnung ersetzt wird.

Keywords

Insurance FairnessSolidarityRisk IndividualizationClassificationBig DataBig DataClassificationÉquité des assurancesSolidaritéIndividualisation du risqueBig DataKlassifizierungausgleichende Gerechtigkeit der VersicherungenSolidaritätRisikoindividualisierung
Type
Research Article
Information
European Journal of Sociology / Archives Européennes de Sociologie , Volume 61 , Issue 2 , August 2020 , pp. 159 - 184
Copyright
© European Journal of Sociology 2020

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