  Many instances of algorithmic bias are caused by distributional shifts. For
example, machine learning (ML) models often perform worse on demographic groups
that are underrepresented in the training data. In this paper, we leverage this
connection between algorithmic fairness and distribution shifts to show that
algorithmic fairness interventions can help ML models overcome distribution
shifts, and that domain adaptation methods (for overcoming distribution shifts)
can mitigate algorithmic biases. In particular, we show that (i) enforcing
suitable notions of individual fairness (IF) can improve the
out-of-distribution accuracy of ML models under the covariate shift assumption
and that (ii) it is possible to adapt representation alignment methods for
domain adaptation to enforce individual fairness. The former is unexpected
because IF interventions were not developed with distribution shifts in mind.
The latter is also unexpected because representation alignment is not a common
approach in the individual fairness literature.
