When systems lack interpretability, organizations face delays, increased oversight, and reduced trust. Engineers struggle to isolate failure modes. Legal and compliance teams lack the visibility ...

AI systems now operate on a very large scale. Modern deep learning models contain billions of parameters and are trained on ...

A new study by Botond Szabo (Bocconi Department of Decision Sciences) lays the cornerstone for more accurate, reliable, and interpretable distributed computing methods. In the world of big data, when ...

Risk calculators are used to evaluate disease risk for millions of patients, making their accuracy crucial. But when national models are adapted for local populations, they often deteriorate, losing ...

A new explainable AI technique transparently classifies images without compromising accuracy. The method, developed at the University of Michigan, opens up AI for situations where understanding why a ...

A research team led by Prof. Li Hai from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a novel deep learning framework that significantly improves the ...

Researchers used advanced machine learning to increase the accuracy of a national cardiovascular risk calculator while preserving its interpretability and original risk associations. Risk calculators ...