As machine learning (ML) and artificial intelligence (AI) are deployed across the worlds of science, business, politics, and digital culture, this series proposes an unusual approach to building the interdisciplinary perspectives that this range of contexts demands. Instead of falling back onto existing disciplinary roles, where the lines between ethical and technical research are clearly demarcated in advance, we will stage a series of ground-level dialogues between a researcher from the natural sciences and engineering with a counterpart from the social sciences and humanities. Each conversation will begin with a concept shared across machine learning and the social sciences, like “bias,” “data,” “inference,” “model,” or “recognition.” In these public conversations, speakers will work to “translate” their own ways of understanding machine learning. They will consider not only how ML and AI are affecting different parts of society but at a more basic level, how they are transforming or conception of science and social relationships in the first place. It is hoped that this series will serve as a starting point to launch new interdisciplinary teams and research projects at Durham.