Transparency vs. Black-Box models in Artificial Intelligence

This nugget explores the contrast between transparency and “black-box”
models in artificial intelligence. It discusses interpretable models
such as Decision Trees, Linear Regression, and Logistic Regression, then
delves into the model-agnostic method SHAP (SHapley Additive
exPlanations) as a solution to explain complex models. The importance of
transparency and explainability in applications like quality control,
predictive maintenance, and supply chain optimization in Industry 4.0 is
highlighted.