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Tuesday, September 10 • 11:00 - 12:00
Data Journalism and the Scientific Method

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Data journalism shares much with science. It's a discipline designed to generate knowledge from empirical observations, and borrows a great deal of quantitative methodology from science. It also suffers from some of the same problems as science: reproducibility, causal identification, measurement problems, and mistakes. But data journalism has additional challenges: reporters are not often trained in science, and the results must be effectively communicated to the public. In this talk I’ll show Workbench, a tool built at Columbia to try to make data work more reproducible and accessible for journalists without technical training, and discuss the future potential of AI for data analysis.

avatar for Jonathan Stray

Jonathan Stray

Research Scholar, Columbia Journalism School
Jonathan Stray is a computational journalist at Columbia university, where he teaches the dual masters degree in computer science and journalism and leads the development of Workbench, an integrated tool for data journalism without coding. Research interests include AI for investigative... Read More →

Tuesday September 10, 2019 11:00 - 12:00 CEST
Großer Saal