AI, insurance, discrimination and unfair differentiation. An overview and research agenda
CoRR(2024)
摘要
Insurers increasingly use AI. We distinguish two situations in which insurers
use AI: (i) data-intensive underwriting, and (ii) behaviour-based insurance.
(i) First, insurers can use AI for data analysis to assess risks:
data-intensive underwriting. Underwriting is, in short, calculating risks and
amending the insurance premium accordingly. (ii) Second, insurers can use AI to
monitor the behaviour of consumers in real-time: behaviour-based insurance. For
example, some car insurers give a discount if a consumer agrees to being
tracked by the insurer and drives safely. While the two trends bring many
advantages, they may also have discriminatory effects. This paper focuses on
the following question. Which discrimination-related effects may occur if
insurers use data-intensive underwriting and behaviour-based insurance? We
focus on two types of discrimination-related effects: discrimination and other
unfair differentiation. (i) Discrimination harms certain groups who are
protected by non-discrimination law, for instance people with certain
ethnicities. (ii) Unfair differentiation does not harm groups that are
protected by non-discrimination law, but it does seem unfair. We introduce four
factors to consider when assessing the fairness of insurance practices. The
paper builds on literature from various disciplines including law, philosophy,
and computer science.
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