Mitigating Discrimination in Insurance with Wasserstein Barycenters
Arthur Charpentier François Hu Philipp Ratz
The insurance industry is heavily reliant on predictions of risks based on
characteristics of potential customers. Although the use of said models is
common, researchers have long pointed out that such practices perpetuate
discrimination based on sensitive features such as gender or race. Given that
such discrimination can often be attributed to historical data biases, an
elimination or at least mitigation is desirable. With the shift from more
traditional models to machine-learning based predictions, calls for greater
mitigation have grown anew, as simply excluding sensitive variables in the
pricing process can be shown to be ineffective. In this article, we first
investigate why predictions are a necessity within the industry and why
correcting biases is not as straightforward as simply identifying a sensitive
variable. We then propose to ease the biases through the use of Wasserstein
barycenters instead of simple scaling. To demonstrate the effects and
effectiveness of the approach we employ it on real data and discuss its
implications.