The Radical Inductiveness of Machine Learning

BIDS + the Computational Text Analysis Working Group (CTAWG)/D-Lab

Lecture

February 26, 2020
3:00pm to 4:00pm
190 Doe Library
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Machine learning is often framed in the social sciences as a more sophisticated way to do regression analysis. In this talk 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.

This talk is being hosted by BIDS and the Computational Text Analysis Working Group (CTAWG) at D-Lab.

Speaker(s)

Laura K. Nelson

Alumni - BIDS Data Science Fellow

Former BIDS Data Science Fellow Laura K. Nelson is an Assistant Professor of Sociology in the College of Social Sciences and Humanities at Northeastern University. Laura uses computational methods and open source tools - principally automated text analysis - to study social movements, culture, gender, institutions, and organizations. She is particularly interested in developing computational tools that can bolster the way social scientists do inductive and theory-driven research. She received her PhD in sociology from the University of California, Berkeley, and she also holds an MA from UC Berkeley and a BA from the University of Wisconsin, Madison. While at UC Berkeley, she was a postdoctoral fellow with Digital Humanities @ Berkeley, developing a course for undergraduates on computational text analysis in the humanities and social sciences.