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.

翻译：保险公司越来越多地使用人工智能。我们区分了保险公司使用人工智能的两种情形：（i）数据密集型核保，以及（ii）基于行为的保险。（i）首先，保险公司可利用人工智能进行数据分析以评估风险，即数据密集型核保。简而言之，核保是计算风险并相应调整保险费的过程。（ii）其次，保险公司可利用人工智能实时监测消费者行为，即基于行为的保险。例如，若消费者同意接受保险公司追踪并安全驾驶，一些车险公司会给予折扣。虽然这两种趋势带来诸多优势，但也可能产生歧视性影响。本文聚焦以下问题：当保险公司采用数据密集型核保和基于行为的保险时，可能产生哪些与歧视相关的影响？我们重点关注两类歧视相关影响：歧视及其他不公平区分。（i）歧视伤害受反歧视法保护的特定群体，例如某些族裔人群。（ii）不公平区分虽不伤害反歧视法保护的群体，但显得不公平。我们引入四个因素来评估保险实践的公平性。本文基于法律、哲学、计算机科学等多个学科的文献展开论述。