On the Inductive Biases of Demographic Parity-based Fair Learning
  Algorithms
Haoyu Lei Amin Gohari Farzan Farnia
Fair supervised learning algorithms assigning labels with little dependence
on a sensitive attribute have attracted great attention in the machine learning
community. While the demographic parity (DP) notion has been frequently used to
measure a model's fairness in training fair classifiers, several studies in the
literature suggest potential impacts of enforcing DP in fair learning
algorithms. In this work, we analytically study the effect of standard DP-based
regularization methods on the conditional distribution of the predicted label
given the sensitive attribute. Our analysis shows that an imbalanced training
dataset with a non-uniform distribution of the sensitive attribute could lead
to a classification rule biased toward the sensitive attribute outcome holding
the majority of training data. To control such inductive biases in DP-based
fair learning, we propose a sensitive attribute-based distributionally robust
optimization (SA-DRO) method improving robustness against the marginal
distribution of the sensitive attribute. Finally, we present several numerical
results on the application of DP-based learning methods to standard centralized
and distributed learning problems. The empirical findings support our
theoretical results on the inductive biases in DP-based fair learning
algorithms and the debiasing effects of the proposed SA-DRO method.