Researchers develop tools to help data scientists make the features used in machine-learning models more understandable for end users.

Graduate student Sarah Cen explores the interplay between humans and artificial intelligence systems, to help build accountability and trust.

Researchers use artificial intelligence to help autonomous vehicles avoid idling at red lights.

For individuals who communicate using a single switch, a new interface learns how they make selections, and then self-adjusts accordingly.

Researchers design a user-friendly interface that helps nonexperts make forecasts using data collected over time.

The computer-vision technique behind these maps could help avoid contrail production, reducing aviation’s climate impact.

A new model shows that the more polarized and hyperconnected a social network is, the more likely misinformation will spread.

A new method forces a machine learning model to focus on more data when learning a task, which leads to more reliable predictions.

A visual analytics tool helps child welfare specialists understand machine learning predictions that can assist them in screening cases.

Researchers hope more user-friendly machine-learning systems will enable nonexperts to analyze big data — but can such systems ever be completely autonomous?