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.

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.

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.

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

Researchers have developed a new explainable artificial intelligence (AI) model to reduce bias and enhance trust and accuracy in machine learning-generated decision-making and knowledge organization.