What makes insurance special among risk technologies is the particular way in which it links solidarity and technical rationality. On one hand, within insurance practices ‘risk’ is always defined in technical terms. It is related to monetary measurement of value and to statistical probability calculated for a limited population. On the other hand, and at the same time, insurance has an inherent connection to solidarity. When taking out an insurance, one participates in the risk pool within which each member is (...) reciprocally responsible for others’ risks. The combination of technical controllability and solidarity has made insurance a successful tool for governing welfare societies during the twentieth century. From the point of view of business ethics, it is interesting that, as we argue in this article, the connection between insurance and solidarity is not limited to social welfare assemblages, but is evident in relation to private insurance as well. At the same time, however, it is important to understand that insurance does not advance all forms of solidarity. Hence, this theoretical article analyzes the specific conceptions of solidarity that the different forms of insurance practice produce. Particular emphasis is put on the distinction between ‘chance solidarity’ and ‘subsidizing solidarity’. The main questions of the article are: What kinds of conceptions of solidarity are built in the insurance technology? And how are the limits of solidarity defined and justified in different forms of insurance? (shrink)

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of false (...) positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness. (shrink)

Insurance is pervasive in many social settings. As a cooperative device based on risk pooling, it serves to attenuate the adverse consequences of various risks by offering policyholders coverage against the losses implied by adverse events in exchange for the payment of premiums. In the insurance industry, the concept of actuarial fairness serves to establish what could be adequate, fair premiums. Accordingly, premiums paid by policyholders should match as closely as possible their risk exposure. Such premiums are the product of (...) the probabilities of losses and the expected losses. This article presents a discussion of the fairness of actuarial fairness through three steps: defining the concept based on its formulation within the insurance industry; determining in which sense it may be about fairness; and raising some objections to the actual fairness of actuarial fairness. The necessity of a normative evaluation of actuarial fairness is justified by the influence of the concept on the current reforms of public insurance systems and the fact that it highlights the question of the repartition of the gains and burdens of social cooperation. (shrink)

Here, we provide an ethical analysis of discrimination in private insurance to guide the application of non-discriminatory algorithms for risk prediction in the insurance context. This addresses the need for ethical guidance of data-science experts, business managers, and regulators, proposing a framework of moral reasoning behind the choice of fairness goals for prediction-based decisions in the insurance domain. The reference to private insurance as a business practice is essential in our approach, because the consequences of discrimination and predictive inaccuracy in (...) underwriting are different from those of using predictive algorithms in other sectors. Here we focus on the trade-off in the extent to which one can pursue indirect non-discrimination versus predictive accuracy. The moral assessment of this trade-off is related to the context of application—to the consequences of inaccurate risk predictions in the insurance domain. (shrink)

The use of algorithmic prediction in insurance is regarded as the beginning of a new era, because it promises to personalise insurance policies and premiums on the basis of individual behaviour and level of risk. The core idea is that the price of the policy would no longer refer to the calculated uncertainty of a pool of policyholders, with the consequence that everyone would have to pay only for her real exposure to risk. For insurance, however, uncertainty is not only (...) a problem – shared uncertainty is a resource. The availability of individual risk information could undermine the principle of risk-pooling and risk-spreading on which insurance is based. The article examines this disruptive change first by exploring the possible consequences of the use of predictive algorithms to set insurance premiums. Will it endanger the principle of mutualisation of risks, producing new forms of discrimination and exclusion from coverage? In a second step, we analyse how the relationship between the insurer and the policyholder changes when the customer knows that the company has voluminous, and continuously updated, data about her real behaviour. (shrink)