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Building trustworthy, effective, and responsible machine learning systemshinges on understanding how differences in training data and modeling decisionsinteract to impact predictive performance. In this work, we seek to betterunderstand how we might characterize, detect, and design for data-modelsynergies. We focus on a particular type of data-model inefficiency, in whichadding training data from some sources can actually lower performance evaluatedon key sub-groups of the population, a phenomenon we refer to as negative dataexternalities on group performance. Such externalities can arise in standardlearning settings and can manifest differently depending on conditions betweentraining set size and model size. Data externalities directly imply a lowerbound on feasible model improvements, yet improving models efficiently requiresunderstanding the underlying data-model tensions. From a broader perspective,our results indicate that data-efficiency is a key component of both accurateand trustworthy machine learning.

Striving for data-model efficiency: Identifying data externalities on group performance