  In applications of predictive modeling, such as insurance pricing, indirect
or proxy discrimination is an issue of major concern. Namely, there exists the
possibility that protected policyholder characteristics are implicitly inferred
from non-protected ones by predictive models, and are thus having an
undesirable (or illegal) impact on prices. A technical solution to this problem
relies on building a best-estimate model using all policyholder characteristics
(including protected ones) and then averaging out the protected characteristics
for calculating individual prices. However, such approaches require full
knowledge of policyholders' protected characteristics, which may in itself be
problematic. Here, we address this issue by using a multi-task neural network
architecture for claim predictions, which can be trained using only partial
information on protected characteristics, and it produces prices that are free
from proxy discrimination. We demonstrate the use of the proposed model and we
find that its predictive accuracy is comparable to a conventional feedforward
neural network (on full information). However, this multi-task network has
clearly superior performance in the case of partially missing policyholder
information.
