Julian Posada is an Assistant Professor of American Studies at Yale University. His research integrates theories and methods from information studies, sociology, and human–computer interaction to study technology and society. His current research focuses on the experiences of outsourced workers in Latin America employed by digital platforms to produce machine learning data and verify algorithmic outputs. Posada’s research has been published in several influential journals, including Information, Communication & Society, and the Proceedings of the ACM on Human-Computer Interaction.
What do you do with data science?
As someone initially trained in the humanities and the social sciences, I explored data science through computational social science initiatives that bring computational methods to social research, notably in studies that involve data collected online. Later, I started investigating data on its own from a social perspective. My work focuses on producing data for machine learning, particularly processes of data generation, annotation, and algorithmic verification that are outsourced through crowdsourcing platforms. I used data science methods to study platforms at a global scale by analyzing online data. This work blends the boundaries between humanistic, social, and computational research, and, in the long term, I want to contribute to these multidisciplinary dialogues. I believe that, in a datafied world, humanistic disciplines need computational insights and vice versa.