Fair Active Learning [article]

Hadis Anahideh and Abolfazl Asudeh and Saravanan Thirumuruganathan
2021 arXiv   pre-print
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
more » ... 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.
arXiv:2001.01796v5 fatcat:u2iufnu4qnh67og5v64hn75dta