Résultats du processus de synthèse de l'article arXiv : 2401.11892v1

Résumé : 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.