A new technique for removing bias in datasets can enable machine-learning models to make loan approval predictions that are both fair and accurate.

MIT AI Hardware Program launches with five inaugural companies to advance AI technologies for the next decade.

A machine-learning model for image classification that’s trained using synthetic data can rival one trained on the real thing, a study shows.

A model’s ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.

A new technique boosts models’ ability to reduce bias, even if the dataset used to train the model is unbalanced.

“If used carefully, this technology could improve performance in health care and potentially reduce inequities,” says Assistant Professor Marzyeh Ghassemi. “But if we’re not actually careful, technology could worsen care.”

Neural networks are sometimes called black boxes because, despite the fact that they can outperform humans on certain tasks, even the researchers who design them often don’t understand how or why they work so well. But if a neural network is used outside the lab, perhaps to classify medical images that could help diagnose heart conditions, knowing how the model works helps researchers predict how it will behave in practice.

The machine-learning model could help scientists speed the development of new medicines.