Elizabeth is a researcher in the Data & Society AI on the Ground research track, where she uses empirical methods to study emergent issues around artificial intelligence. Her research illuminates how the introduction of AI into sociotechnical systems produces novel kinds of human labor and unforeseen risks.
Currently pursuing a doctorate in Communications at Columbia University and advised by David Stark, she was trained as an organizational ethnographer, and conducts interviews, digital ethnography, and surveys to study how people interpret, use, and talk about algorithms in their jobs. She’s writing a dissertation examining how gig workers negotiate facial recognition, and has presented her research at conferences including Computer-Human Interaction (CHI), Computer Supported Cooperative Work (CSCW), security conference USENIX, and the annual meetings of the International Communications Association (ICA), the International Sociological Association (ISA), and the Society for the Social Studies of Science (4S).
She’s also a Research Associate at the Tow Center for Digital Journalism at Columbia, a member of the Center on Organizational Innovation at Columbia, and a member of the Future of Work and Organizations interest group at NYU Stern. She writes for the NYC art collective American Cyborg about animals, machines, and metaphors. She holds a BA from the University of California at Irvine and a Master of Science from the MIT School of Architecture + Planning. Before starting her doctorate she worked for several years as a researcher at Harvard Business School. Her case studies published by Harvard Business Publishing have been taught at HBS, the Yale School of Management, and the MIT Sloan School of Management.