  Research at the intersection of machine learning and the social sciences has
provided critical new insights into social behavior. At the same time, a
variety of critiques have been raised ranging from technical issues with the
data used and features constructed, problematic assumptions built into models,
their limited interpretability, and their contribution to bias and inequality.
We argue such issues arise primarily because of the lack of social theory at
various stages of the model building and analysis. In the first half of this
paper, we walk through how social theory can be used to answer the basic
methodological and interpretive questions that arise at each stage of the
machine learning pipeline. In the second half, we show how theory can be used
to assess and compare the quality of different social learning models,
including interpreting, generalizing, and assessing the fairness of models. We
believe this paper can act as a guide for computer and social scientists alike
to navigate the substantive questions involved in applying the tools of machine
learning to social data.
