Insurance, Algorithmic Decision-Making, and Discrimination

Abstract

Insurance companies could use algorithmic systems to set premiums for individual consumers or deny them insurance. More and more data become available for insurers for risk differentiation. For example, some insurers monitor people's driving behaviour to estimate risks. To some extent, risk differentiation is necessary for insurance. And it could be considered fair when, e.g., high-risk drivers pay more. But there are drawbacks. Algorithmic decision-making could lead, unintentionally, to discrimination on the basis of, for instance, ethnicity or gender. Too much personalized risk differentiation could also make insurance unaffordable for some people. Furthermore, risk differentiation might result in the poor paying more, thereby worsening economic inequality. We address these topics with a three-part workshop: -Part 1: Panel (90min) -Part 2: Break-out groups (60min) -Part 3: Presentations (60min)