  Collecting more diverse and representative training data is often touted as a
remedy for the disparate performance of machine learning predictors across
subpopulations. However, a precise framework for understanding how dataset
properties like diversity affect learning outcomes is largely lacking. By
casting data collection as part of the learning process, we demonstrate that
diverse representation in training data is key not only to increasing subgroup
performances, but also to achieving population level objectives. Our analysis
and experiments describe how dataset compositions influence performance and
provide constructive results for using trends in existing data, alongside
domain knowledge, to help guide intentional, objective-aware dataset design.
