Multi-year datasets suggest projecting outcomes of people’s lives with AI isn’t so simple

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.

Published by 112 co-authors in the Proceedings of the National Academy of Sciences, the results suggest that sociologists and data scientists should use caution in predictive modeling, especially in the criminal justice system and social programs.

One hundred and sixty research teams of data and social scientists built statistical and machine-learning models to predict measure six life outcomes for children, parents and households. Even after using a state-of-the-art modeling and a high-quality dataset containing 13,000 data points about more than 4,000 families, the best AI predictive models were not very accurate.

“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 study co-lead author Matt Salganik, professor of sociology

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