  Robustness is of central importance in machine learning and has given rise to
the fields of domain generalization and invariant learning, which are concerned
with improving performance on a test distribution distinct from but related to
the training distribution. In light of recent work suggesting an intimate
connection between fairness and robustness, we investigate whether algorithms
from robust ML can be used to improve the fairness of classifiers that are
trained on biased data and tested on unbiased data. We apply Invariant Risk
Minimization (IRM), a domain generalization algorithm that employs a causal
discovery inspired method to find robust predictors, to the task of fairly
predicting the toxicity of internet comments. We show that IRM achieves better
out-of-distribution accuracy and fairness than Empirical Risk Minimization
(ERM) methods, and analyze both the difficulties that arise when applying IRM
in practice and the conditions under which IRM will likely be effective in this
scenario. We hope that this work will inspire further studies of how robust
machine learning methods relate to algorithmic fairness.
