Molnar has written the book "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable", in which he elaborates on the issue and examines methods for achieving explainability.

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.

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

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

Tech GSA challenge found industry machine-learning models can make do with limited training data Techniques like transfer learning have come a long way and were used to fine-tune models so they could ...