Crowdsourcing with Fairness, Diversity and Budget Constraints
  Recent studies have shown that the labels collected from crowdworkers can be
discriminatory with respect to sensitive attributes such as gender and race.
This raises questions about the suitability of using crowdsourced data for
further use, such as for training machine learning algorithms. In this work, we
address the problem of fair and diverse data collection from a crowd under
budget constraints. We propose a novel algorithm which maximizes the expected
accuracy of the collected data, while ensuring that the errors satisfy desired
notions of fairness. We provide guarantees on the performance of our algorithm
and show that the algorithm performs well in practice through experiments on a
real dataset.
