Cornell Bowers College of Computing and Information Science

Kizilcec: Testing AI fairness in predicting college dropout rate

To help struggling college students before it is too late, more and more universities are adopting machine-learning models to identify students at risk of dropping out.

What information goes into these models can have a big effect on how accurate and fair they are, especially when it comes to protected student characteristics like gender, race and family income. But in a new study, the largest audit of a college AI system to date, researchers find no evidence that removing protected student characteristics from a model improves the accuracy or fairness of predictions.

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