  In this paper, we consider a theoretical model for injecting data bias,
namely, under-representation and label bias (Blum & Stangl, 2019). We
empirically study the effect of varying data biases on the accuracy and
fairness of fair classifiers. Through extensive experiments on both synthetic
and real-world datasets (e.g., Adult, German Credit, Bank Marketing, COMPAS),
we empirically audit pre-, in-, and post-processing fair classifiers from
standard fairness toolkits for their fairness and accuracy by injecting varying
amounts of under-representation and label bias in their training data (but not
the test data). Our main observations are: 1. The fairness and accuracy of many
standard fair classifiers degrade severely as the bias injected in their
training data increases, 2. A simple logistic regression model trained on the
right data can often outperform, in both accuracy and fairness, most fair
classifiers trained on biased training data, and 3. A few, simple fairness
techniques (e.g., reweighing, exponentiated gradients) seem to offer stable
accuracy and fairness guarantees even when their training data is injected with
under-representation and label bias. Our experiments also show how to integrate
a measure of data bias risk in the existing fairness dashboards for real-world
deployments.
