  Machine learning (ML) is increasingly being used in high-stakes applications
impacting society. Therefore, it is of critical importance that ML models do
not propagate discrimination. Collecting accurate labeled data in societal
applications is challenging and costly. Active learning is a promising approach
to build an accurate classifier by interactively querying an oracle within a
labeling budget. We design algorithms for fair active learning that carefully
selects data points to be labeled so as to balance model accuracy and fairness.
We demonstrate the effectiveness and efficiency of our proposed algorithms over
widely used benchmark datasets using demographic parity and equalized odds
notions of fairness.
