The Radical Inductiveness of Machine Learning
Laura K. Nelson's talk addresses the epistemology of machine learning in light of long-standing debates about quantitative and qualitative methods in the social sciences.
Event, Research Seminar
Salle du LIEPP, 254 Boulevard Saint-Germain, 75007 Paris
Machine learning is typically framed in the social sciences as a more sophisticated way to do regression analysis. I argue that this is an epistemological distortion: the mathematical assumptions behind machine learning are much closer to the epistemology of inductive methods than they are to the deductive requirements of regression analysis. Using examples from my own research, I show that machine learning can not only be used in qualitative and interpretive research, it is, down to its most basic assumptions, a radically inductive method.
Laura K. Nelson Bio
Laura K. Nelson is an assistant professor of sociology at Northeastern University where she is core faculty at the NULab for Texts, Maps, and Networks, is affiliated faculty at the Network Science Institute, and is on the executive committee for Women’s, Gender, and Sexuality Studies.
She was previously a postdoctoral fellow at the Berkeley Institute for Data Science and Digital Humanities @ Berkeley at the University of California, Berkeley, and for the Management and Organizations Department at Northwestern University, where she was also affiliated with the Northwestern Institute on Complex Systems (NICO).
She uses computational tools, principally automated text analysis, to study social movements, culture, gender, institutions, and organizations.