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.

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

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

What is explainable AI? Explainable AI (XAI), also called interpretable AI, refers to machine learning and deep learning methods that can explain their decisions in a way that humans can understand.

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.

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