  Existing algorithms for ensuring fairness in AI use a single-shot training
strategy, where an AI model is trained on an annotated training dataset with
sensitive attributes and then fielded for utilization. This training strategy
is effective in problems with stationary distributions, where both training and
testing data are drawn from the same distribution. However, it is vulnerable
with respect to distributional shifts in the input space that may occur after
the initial training phase. As a result, the time-dependent nature of data can
introduce biases into the model predictions. Model retraining from scratch
using a new annotated dataset is a naive solution that is expensive and
time-consuming. We develop an algorithm to adapt a fair model to remain fair
under domain shift using solely new unannotated data points. We recast this
learning setting as an unsupervised domain adaptation problem. Our algorithm is
based on updating the model such that the internal representation of data
remains unbiased despite distributional shifts in the input space. We provide
extensive empirical validation on three widely employed fairness datasets to
demonstrate the effectiveness of our algorithm.
