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 stereotypes, non-causal correlations, or causal effects that a society chooses to protect against, are thus the main sources (...) of dispute. The lens of this typology then allows us to look anew at the potential biases in insurance pricing implied by big data and machine learning, showing that despite utopic claims, social stereotypes continue to plague data, thus threaten to unconsciously reproduce these discriminations in insurance. To counter these effects, algorithmic fairness attempts to define mathematical indicators of non-bias. We argue that this may prove insufficient, since as it assumes the existence of specific protected groups, which could only be made visible through public debate and contestation. These are less likely if the right to explanation is realized through personalized algorithms, which could reinforce the individualized perception of the social that blocks rather than encourages collective mobilization. (shrink)

In recent years, insurance companies have begun tracking their customers’ behaviors and price premiums accordingly. Based on the Market-Failures Approach as well as the Justice-Failures Approach, I provide an ethical analysis of the use of tracking technologies in the insurance industry. I focus on the use of telematics in car insurance and on the use of fitness tracking in life insurance. The use of tracking has some important benefits to policyholders and insurers alike: it reduces moral hazard and fraud, increases (...) actuarial fairness, and incentivizes safe behavior. These benefits, however, are outweighed by significant moral objections. First, the use of tracking technologies significantly undermines the fairness of the interaction between insurance companies and policyholders. Specifically, the use of tracking eliminates information asymmetries, but in such a way that favors insurers exclusively. Furthermore, tracked behaviors and the ability to choose to behave safely are highly correlated with other variables such as income. Therefore, tracking-based insurance relies on and exacerbates existing inequalities and injustices, which undermines the benefits that tracking is supposed to introduce. (shrink)

This paper offers an ethical analysis of online steering, the practice of personalizing search results in e-commerce based on data about users. We first outline the parallels and differences between online steering and price discrimination, arguing that online steering is more likely to benefit consumers and enhance social welfare than price discrimination. Next, we argue that while online steering does not violate any plausible specification of the equal-treatment norm, it involves an element of manipulation that is absent in price discrimination (...) and that by itself raises ethical concerns regardless of the welfare effects of online steering. We conclude that in order to address these ethical concerns, online steering needs to satisfy a transparency condition. (shrink)