Talk
Avoiding indirect discrimination in modeling mortality
Talk by Dr. Benedikt Herwerth
Date: 28.04.23 Time: 15.15 - 16.15 Room: ETH HG G 19.1
The topic of potential discrimination in statistical and machine learning (ML) is being increasingly discussed, both by the scientific community and the wider public. In the insurance industry specifically, customers and regulators demand that individuals are treated fairly. Regulations of the European Union, for example, mandate that gender is not be used as a factor in determining the prices of policies. This talk is based on a method introduced in a series of papers by Lindholm et al. on "discrimination-free insurance pricing" that address specifically the issue of potential indirect discrimination [1, 2, 3]. In the first part of the talk, we outline the solution by Lindholm et al., and we discuss that indirect discrimination is a topic that is subtle and can be difficult to understand for decision takers. In the second part of our talk, we present a Swiss Re internally built toolbox implementing the methodology. In the third part of our talk, we apply the methodology to model human mortality. We use public data of the German association of actuaries, which we interpret in terms of a Bayesian network describing the relation between age, gender, the smoker status and the mortality of individuals. [1] M. Lindholm, R. Richman, A. Tsanakas and M. V. Wuthrich, "Discrimination-free insurance pricing," ASTIN Bulletin: The Journal of the IAA, vol. 52, pp. 55 - 89, 2022. [2] M. Lindholm, R. Richman, A. Tsanakas and M. V. Wuthrich, "A Multi-Task Network Approach for Calculating Discrimination-Free Insurance Prices," 2 11 2022. [Online]. Available: https://ssrn.com/abstract=4155585. [3] M. Lindholm, R. Richman, A. Tsanakas and M. V. Wuthrich, "A Discussion of Discrimination and Fairness in Insurance Pricing," 2 09 2022. [Online]. Available: https://ssrn.com/abstract=4207310.