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CITP Seminar: Amy Winecoff – Today’s Machine Learning Needs Yesterday’s Social Science

Tuesday, April 19, 2022
12:30 pm - 1:30 pm


Photo Amy Winecoff

Video available here.

Research on machine learning (ML) algorithms, as well as on their ethical impacts, has focused largely on mathematical or computational questions. However, for algorithmic systems to be useful, reliable, and safe for human users, ML research must also wrangle with how users’ psychology and social context affect how they interact with algorithms. This talk will address how novel research on how people interact with ML systems can benefit from decades-old ideas in social science. The first part of the talk will address how well-worn ideas from psychology and behavioral research methods can inform how ML researchers develop and evaluate algorithmic systems. The second part of the talk will address how foundational ideas from organizational and institutional theory can help ML ethicists develop tools and interventions that have practical utility in tomorrow’s real-world technology.


Amy Winecoff is a DataX data scientist at CITP. Her primary interests are in human-algorithm interactions and fairness in machine learning systems. Winecoff received her Ph.D. in psychology and neuroscience from Duke University. After graduate school, she was an assistant professor at Bard College, where she taught neuroscience, abnormal psychology, and research methods. After leaving academia, she conducted research and developed machine learning models for government agencies such as DARPA and the U.S. Air Force to explain and predict human behavior. As a senior data scientist at True Fit and Chewy, she developed product recommendation and search systems. She also conducted quantitative user research to assess how users’ psychology informs their evaluation of algorithmic predictions. Winecoff is passionate about diversity and inclusion in the technology industry.

This seminar is co-sponsored by the Center for Statistics and Machine Learning.

Center for Statistics and Machine Learning