From Tuesday to Friday, I will attend the 60th Actuarial Research Conference in Toronto. With Olivier and Marie-Pier Côté, we will give a series of talk on fairness and discrimination.
I will talk in an Invited Session on Artificial Intelligence in Insurance (as well as Marie-Pier Côté)
Olivier Côté will present in Session 1 – Bias in Assessing Financial Risk
With Olivier and Marie-Pier, we will present in one of the Casualty Actuarial Society Sponsored Sessions, Session 1 – A Scalable Toolbox for Exposing Indirect Discrimination in Insurance Rates
This Friday, I will give a talk in Paris, on using optimal transport to mitigate unfair predictions, at the ARC Seminar.
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 talk, 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. The talk will be based on recent work with François Hu and Philipp Ratz (2310.20508, 2309.06627, 2306.12912 and 2306.10155).