MIT Study Shows Dangers of Relying on Aggregate Machine Learning Metrics in Real-World Deployments
A new MIT study reveals how even high-performing machine learning models can fail dramatically in new settings, urging the use of more granular metrics to avoid hidden risks and biased outcomes—particularly in sensitive domains like healthcare.