In my previous article, I discussed the importance of AI explainability and the different categories of AI explainability, explainable predictions, explainable algorithms and interpretable ...

Explainable AI (XAI) exists to close this gap. It is not just a trend or an afterthought; XAI is an essential product capability required for responsibly scaling AI. Without it, AI remains a powerful ...

When AI falters, it’s easy to blame the model. People assume the algorithm got it wrong or that the technology can’t be trusted. But here’s what I've learned after years of building AI systems at ...

Explainable AI provides human users with tools to understand the output of machine learning algorithms. One of these tools, feature attributions, enables users to know the contribution of each feature ...

While atmospheric turbulence is a familiar culprit of rough flights, the chaotic movement of turbulent flows remains an unsolved problem in physics. To gain insight into the system, a team of ...

(From left) Sanket Deshmukh, associate professor in chemical engineering, and Fangxi "Toby" Wang, research scientist in chemical engineering, discussing results of explainable artificial intelligence ...

An area of great hope and promise for applied artificial intelligence (AI) deep learning is at the intersection of neuroscience and oncology, both challenging fields known for their inherent ...

NIMS has been developing chemical sensors as a key component of artificial olfaction technology (olfactory sensors), with the aim of putting this technology into practical use. In a new study, ...

A team has developed an explainable AI model for automatic collision avoidance between ships. The Titanic sunk 113 years ago on April 14-15, after hitting an iceberg ...