  Training a fair machine learning model is essential to prevent demographic
disparity. Existing techniques for improving model fairness require broad
changes in either data preprocessing or model training, rendering themselves
difficult-to-adopt for potentially already complex machine learning systems. We
address this problem via the lens of bilevel optimization. While keeping the
standard training algorithm as an inner optimizer, we incorporate an outer
optimizer so as to equip the inner problem with an additional functionality:
Adaptively selecting minibatch sizes for the purpose of improving model
fairness. Our batch selection algorithm, which we call FairBatch, implements
this optimization and supports prominent fairness measures: equal opportunity,
equalized odds, and demographic parity. FairBatch comes with a significant
implementation benefit -- it does not require any modification to data
preprocessing or model training. For instance, a single-line change of PyTorch
code for replacing batch selection part of model training suffices to employ
FairBatch. Our experiments conducted both on synthetic and benchmark real data
demonstrate that FairBatch can provide such functionalities while achieving
comparable (or even greater) performances against the state of the arts.
Furthermore, FairBatch can readily improve fairness of any pre-trained model
simply via fine-tuning. It is also compatible with existing batch selection
techniques intended for different purposes, such as faster convergence, thus
gracefully achieving multiple purposes.
