Indirect discrimination is an issue of major concern in algorithmic models.
This is particularly the case in insurance pricing where protected policyholder
characteristics are not allowed to be used for insurance pricing. Simply
disregarding protected policyholder information is not an appropriate solution
because this still allows for the possibility of inferring the protected
characteristics from the non-protected ones. This leads to so-called proxy or
indirect discrimination. Though proxy discrimination is qualitatively different
from the group fairness concepts in machine learning, these group fairness
concepts are proposed to 'smooth out' the impact of protected characteristics
in the calculation of insurance prices. The purpose of this note is to share
some thoughts about group fairness concepts in the light of insurance pricing
and to discuss their implications. We present a statistical model that is free
of proxy discrimination, thus, unproblematic from an insurance pricing point of
view. However, we find that the canonical price in this statistical model does
not satisfy any of the three most popular group fairness axioms. This seems
puzzling and we welcome feedback on our example and on the usefulness of these
group fairness axioms for non-discriminatory insurance pricing.Comment: 14 page