Algorithmic Decision Making and Model Explainability Preferences in the Insurance Industry: A Delphi Study

Eric Schotman, Y.D. Iren

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review


There is growing attention to the transparency, fairness, and explainability of algorithmic decision-making systems as they permeate many aspects of our lives. Despite the awareness of the need for algorithmic transparency and the right-to-meaningful-explanation provided by GDPR, little is known regarding what makes such explanations meaningful and useful. This issue becomes especially challenging in certain situations in which high levels of transparency may conflict with the best interest of organizations. The insurance industry poses an interesting case as the business model of insurance providers depends on the discrimination of customer groups. In this paper, we present the results of a Delphi study with experts from the Dutch insurance industry informed by an initial survey. Our results include the preferred explanation elements towards customers, from the perspective of the insurer, for five commonly used algorithmic decision-making systems. They also show that there is not a one-size-fits-all explanation approach, and that it depends on the system itself and the context in which it is used. Finally, the results highlight risks and concerns of the insurance experts regarding the disclosure of sensitive information in the form of explanations.

Original languageEnglish
Title of host publication2022 IEEE 24th Conference on Business Informatics (CBI)
Number of pages8
ISBN (Electronic)978-1-6654-6016-3
ISBN (Print)978-1-6654-6038-5
Publication statusPublished - 15 Nov 2022
Event24th IEEE International Conference on Business Informatics - Amsterdam, Netherlands
Duration: 15 Jun 202217 Jun 2022
Conference number: 24


Conference24th IEEE International Conference on Business Informatics
Abbreviated titleCBI 2022
Internet address


  • Delphi study
  • algorithmic decision making
  • explainability
  • insurance


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