  Dataset bias is one of the prevailing causes of unfairness in machine
learning. Addressing fairness at the data collection and dataset preparation
stages therefore becomes an essential part of training fairer algorithms. In
particular, active learning (AL) algorithms show promise for the task by
drawing importance to the most informative training samples. However, the
effect and interaction between existing AL algorithms and algorithmic fairness
remain under-explored. In this paper, we study whether models trained with
uncertainty-based AL heuristics such as BALD are fairer in their decisions with
respect to a protected class than those trained with identically independently
distributed (i.i.d.) sampling. We found a significant improvement on predictive
parity when using BALD, while also improving accuracy compared to i.i.d.
sampling. We also explore the interaction of algorithmic fairness methods such
as gradient reversal (GRAD) and BALD. We found that, while addressing different
fairness issues, their interaction further improves the results on most
benchmarks and metrics we explored.
