The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Demo's of FairLearn and InterpretML as described in my article on responsible AI.

Insurance workflow wrapper for interpretML's Explainable Boosting Machine — relativities, diagnostics, monotonicity editing, GLM comparison

This project applies Microsoft's InterpretML library to analyze Titanic survival data using two ML models: the Explainable Boosting Machine (EBM), a fully transparent glassbox model, and Random Forest, a blackbox model. The EBM allows each prediction to be traced back to specific features such as gender, age, and passenger class.