On the issue of insurance discrimination, a grey area in regulation has resulted from the growing use of big data analytics by insurance companies: direct discrimination is prohibited, but indirect discrimination using proxies or more complex and opaque algorithms is not clearly specified or assessed. This phenomenon has recently attracted the attention of insurance regulators all over the world. Meanwhile, various fairness criteria have been proposed and flourished in the machine learning literature with the rapid growth of artificial intelligence (AI) in the past decade and have mostly focused on classification decisions. In this article, we introduce some fairness criteria that are potentially applicable to insurance pricing as a regression problem to the actuarial field, match them with different levels of potential and existing antidiscrimination regulations, and implement them into a series of existing and newly proposed antidiscrimination insurance pricing models, using both generalized linear models (GLMs) and Extreme Gradient Boosting (XGBoost). Our empirical analysis compares the outcome of different models via the fairness–accuracy trade-off and shows their impact on adverse selection and solidarity.

在保险歧视问题上，由于保险公司越来越多地使用大数据分析，监管出现了灰色地带：禁止直接歧视，但没有明确规定或评估使用代理或更复杂和不透明算法的间接歧视。这一现象近期引起了世界各地保险监管机构的关注。与此同时，随着过去十年人工智能（AI）的快速发展，各种公平标准在机器学习文献中被提出并蓬勃发展，并且主要集中在分类决策上。在本文中，我们介绍了一些可能适用于保险定价的公平标准，作为精算领域的回归问题，将它们与不同级别的潜在和现有反歧视法规相匹配，并使用广义线性模型（GLM）和极限梯度提升（XGBoost）将它们实施到一系列现有和新提出的反歧视保险定价模型中。我们的实证分析通过公平性与准确性权衡比较了不同模型的结果，并显示了它们对逆向选择和团结的影响。