  Building trustworthy, effective, and responsible machine learning systems
hinges on understanding how differences in training data and modeling decisions
interact to impact predictive performance. In this work, we seek to better
understand how we might characterize, detect, and design for data-model
synergies. We focus on a particular type of data-model inefficiency, in which
adding training data from some sources can actually lower performance evaluated
on key sub-groups of the population, a phenomenon we refer to as negative data
externalities on group performance. Such externalities can arise in standard
learning settings and can manifest differently depending on conditions between
training set size and model size. Data externalities directly imply a lower
bound on feasible model improvements, yet improving models efficiently requires
understanding the underlying data-model tensions. From a broader perspective,
our results indicate that data-efficiency is a key component of both accurate
and trustworthy machine learning.
