Emory University / QTM 310 / Spring 2023
This course, team-taught by faculty spanning the various areas represented in QTM, will introduce students to both theoretical and methodological issues related to data justice. This emerging field considers how questions about data, its collection, and its use, are connected to broader social and political concerns, and how data-driven systems can be designed more equitably. Such data is expansive and expanding, and serves as the basis for automated systems that range from resume screening to voting redistricting, predictive policing to cell-phone autocomplete.
A central theme of the course is that choices (and trade-offs) are ubiquitous when bringing data to bear on technical and policy decisions. Few, if any, meaningful measures are truly “theory-free,’’ in the sense that a measure is (perhaps implicitly) measuring something, and in many cases, this something is latent and not directly observable. Furthermore, even seemingly objective algorithmic systems may not be “neutral” in their effects: many of these systems rely upon data or were designed to achieve goals that reflect existing biases and inequalities embedded in the world.
Upon completing this course, students will be able to define and discuss the concepts of bias, fairness, discrimination, ethics, and justice, with respect to data science, and will gain familiarity, via case studies and labs, with how these concepts play out in data-driven inquiry.