Computational Social Science ≠ Computer Science + Social Data | March 2018 | Communications of the ACM

To me, then, this highlights an important path forward. Clearly, machine learning is incredibly useful—and, in particular, machine learning is useful for social science. But we must treat machine learning for social science very differently from the way we treat machine learning for, say, handwriting recognition or playing chess. We cannot just apply machine learning methods in a black-box fashion, as if computational social science were simply computer science plus social data. We need transparency. We need to prioritize interpretability—even in predictive contexts. We need to conduct rigorous, detailed error analyses. We need to represent uncertainty. But, most importantly, we need to work with social scientists in order to understand the ethical implications and consequences of our modeling decisions.