Explainable AI: From the peak of inflated expectations to the pitfalls of interpreting machine learning models We have reached peak hype for explainable AI.

Explainable AI: A guide for making black box machine learning models explainable In the future, AI will explain itself, and interpretability could boost machine intelligence research.

As machine learning techniques become increasingly used in the sciences, a team of researchers in Lawrence Livermore National Laboratory's Computing and - Read more from Inside HPC & AI News.

The growing trend of AI means that it’s business-critical to understand how AI-enabled systems arrive at specific outputs.

Chain Of Thought Models Machine learning models are nothing more than incredibly complex functions with billions, and now even trillions of learned parameters.

One major difference between standard AI and explainable AI is that traditional AI models often behave like "black boxes"—they generate predictions, but we don't always understand how or why ...

Explainable AI, abbreviated "XAI," is an emerging set of techniques to peel back the curtains on complex AI systems.

“Explainable AI” can bridge the gap between AI outputs and human expertise, but a balance needs to be struck between explainability and performance.