Our paper, Melting contestation: insurance fairness and machine learning, with Laurence Barry, is now published (in Ethics and Information Technology). With their intensive use of data to classify and price risk, insurers have often been confronted with data-related issues of fairness and discrimination. This paper provides a comparative review of discrimination issues raised by traditional statistics versus machine learning in the context of insurance. We first examine historical contestations of insurance classification, showing that it was organized along three types of bias: pure …