The machine learning techniques scientists use to predict outcomes from large datasets may fall short when it comes to projecting the outcomes of people’s lives, according to a mass collaborative study led by researchers at Princeton. CITP Interim Director Matthew Salganik is one of 112 co-authors of research published in the Proceedings of the National Academy of Sciences. “Here’s a setting where we have hundreds of participants and a rich dataset, and even the best AI results are still not accurate,” said Salganik.

CITP/CS graduate student, Claudia Roberts, also worked on this research and was challenged by Salganik to look at the data as a social scientist. Roberts said the exercise was a good reminder of how complex humans are, which may be hard for machine learning to model. “We want these machine learning models to unearth patterns in massive datasets that we, as humans, don’t have the bandwidth or ability to detect.

Access the full article written by B. Rose Huber of the Woodrow Wilson School of Public and International Affairs.