  Recent attempts to achieve fairness in predictive models focus on the balance
between fairness and accuracy. In sensitive applications such as healthcare or
criminal justice, this trade-off is often undesirable as any increase in
prediction error could have devastating consequences. In this work, we argue
that the fairness of predictions should be evaluated in context of the data,
and that unfairness induced by inadequate samples sizes or unmeasured
predictive variables should be addressed through data collection, rather than
by constraining the model. We decompose cost-based metrics of discrimination
into bias, variance, and noise, and propose actions aimed at estimating and
reducing each term. Finally, we perform case-studies on prediction of income,
mortality, and review ratings, confirming the value of this analysis. We find
that data collection is often a means to reduce discrimination without
sacrificing accuracy.
