In the 1980s a new, extraordinarily productive way of reasoning about algorithms emerged. Though this type of reasoning has come to dominate areas of data science, it has been under-discussed and its impact under-appreciated. For example, it is the primary way we reason about "black box'' algorithms. In this talk we discuss its current use (i.e., as "the common task framework'') and its limitations; we find a large class of prediction-problems are inappropriate for this type of reasoning. Further, we find the common task framework does not provide a foundation for the deployment of an...

Unobserved interactions between people and groups play a fundamental role in domestic and international politics. Yet, despite their importance, the vast complexity of these unobserved interactions has typically frustrated efforts to quantify them, forcing scholars to assume that the units in an analysis are independent or to study a limited range of interactions. Here, I develop a framework and machine learning model for detecting and characterizing unobserved interference dynamics using all available information: outcome, covariate, and independent variable data. Given minimal...