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Highway to (Digital) Surveillance: When Are Clients Coerced to Share Their Data with Insurers?

Author

Listed:
  • Michele Loi

    (University of Zurich
    University of Zurich)

  • Christian Hauser

    (University of Applied Sciences of the Grisons)

  • Markus Christen

    (University of Zurich
    University of Zurich)

Abstract

Clients may feel trapped into sharing their private digital data with insurance companies to get a desired insurance product or premium. However, private insurance must collect some data to offer products and premiums appropriate to the client’s level of risk. This situation creates tension between the value of privacy and common insurance business practice. We argue for three main claims: first, coercion to share private data with insurers is pro tanto wrong because it violates the autonomous choice of a privacy-valuing client. Second, we maintain that irrespective of being coerced, the choice of accepting digital surveillance by insurers makes it harder for the client to protect his or her autonomy (and to act spontaneously and authentically). The violation of autonomy also makes coercing customers into digital surveillance pro tanto morally wrong. Third, having identified an economically plausible process involving no direct coercion by insurers, leading to the adoption of digital surveillance, we argue that such an outcome generates further threats against autonomy. This threat provides individuals with a pro tanto reason to prevent this process. We highlight the freedom dilemma faced by regulators who aim to prevent this outcome by constraining market freedoms and argue for the need for further moral and empirical research on this question.

Suggested Citation

  • Michele Loi & Christian Hauser & Markus Christen, 2022. "Highway to (Digital) Surveillance: When Are Clients Coerced to Share Their Data with Insurers?," Journal of Business Ethics, Springer, vol. 175(1), pages 7-19, January.
  • Handle: RePEc:kap:jbuset:v:175:y:2022:i:1:d:10.1007_s10551-020-04668-1
    DOI: 10.1007/s10551-020-04668-1
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    References listed on IDEAS

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    Cited by:

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    2. Martin Eling & Irina Gemmo & Danjela Guxha & Hato Schmeiser, 2024. "Big data, risk classification, and privacy in insurance markets," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(1), pages 75-126, March.

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