This paper presents a viewpoint on an emerging dichotomy in data science: applications in which predictions of data- driven algorithms are used to support people in making consequential decisions that can have a profound effect on other people’s lives and applications in which data-driven algorithms act autonomously in settings of low consequence and large scale. An example of the first type of application is prison sentencing and of the second type is selecting news stories to appear on a person’s web portal home page. It is argued that the two types of applications require data, al- gorithms and models with vastly different properties along several dimensions, including privacy, equitability, robust- ness, interpretability, causality, and openness. Furthermore, it is argued that the second type of application cannot al- ways be used as a surrogate to develop methods for the first type of application. To contribute to the development of methods for the first type of application, one must really be working on the first type of application.