Two years ago, my book “Insurance, Biases, Discrimination and Fairness” was published in the Springer Actuarial series.
Discrimination in insurance is a difficult topic because, in a very specific sense, insurers are expected to discriminate: they classify risks, build risk pools, and differentiate premiums. This is the logic of risk-based pricing and actuarial fairness. But insurance is not only about pricing risks accurately (accuracy is overrated). It is also about mutualization, risk sharing, and solidarity. The real question is therefore not simply whether insurers should differentiate, but which differences should matter, which should not, where the limits should be drawn, and how to navigate a complex world in which several notions and metrics of fairness coexist, sometimes in tension with one another.
In the book, I tried to connect actuarial pricing, statistical discrimination, legal constraints, algorithmic fairness, explainability, mitigation techniques, and the limits of “fairness through unawareness”. I also discuss a dimension that is often overlooked: causality. Correlation may be useful for prediction, but prevention, explanation, and fairness often require asking what mechanism lies behind an observed association.
For those interested in the mathematics, I have also made lecture notes freely available.
