Cornell University
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Cornell Researchers to Explore How Humans, Machines Make Sense of Big Data

Our information age has yielded a glut of data. No surprise then that data analytics has become widely popular, since, by definition, analytics leverages machine learning algorithms to make sense of data at a scale beyond human abilities. But while computing is essential to data analytics, it also requires human work to make it work, like deciding what data to collect, pre-processing it to make it algorithm-ready, deciding on trade-offs to improve efficiency, developing a testing methodology, and making sense of the results.

This partnership of people and machines is at the heart of a recent National Science Foundation grant awarded to two Cornell Info Science professors. Phoebe Sengers and David Mimno will set out to answer how people and machines can work together more effectively to make sense of large-scale data.

“What I'm particularly excited about is the integration in this work between qualitative social science and data analytics - two approaches that are usually not brought together,” Sengers said.

Through a collaboration between sociologists of technology and data scientists, the Cornell researchers, including Info Science PhD student Samir Passi, will use qualitative social science techniques to identify, track and analyze the human work involved in data analytics for the Digital Humanities, and use this analysis to develop new methods for data analytics research and training.