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Fair Active Learning.
journal contribution
posted on 2021-08-16, 16:52 authored by Hadis AnahidehHadis Anahideh, Abolfazl AsudehAbolfazl Asudeh, Saravanan ThirumuruganathanMachine 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.
Specifically, we focus on demographic parity - a widely used measure of
fairness. Extensive experiments over benchmark datasets demonstrate the
effectiveness of our proposed approach.