White box AI as an alternative

White box AI (also called explainable AI or XAI) is the opposite of black box AI. These models are designed to be transparent and understandable. They include simpler approaches like decision trees, which show clear if-then rules, and linear models that clearly show how much each factor influences the final result. These transparent models allow users to see exactly how inputs connect to outputs. While people often assume white box models are less accurate than black box ones, research shows they can perform just as well in many cases. For high-stakes decisions, the small accuracy gains a black box might offer are often not worth losing the ability to explain, verify, and fix the model. White box models build trust, make troubleshooting easier, and help meet legal requirements for transparency.^[1]