moDel Agnostic Language for Exploration and eXplanation

A toolkit that streamlines and automates the generation of model cards

A detailed summary of "Designing Machine Learning Systems" by Chip Huyen. This book gives you and end-to-end view of all the steps required to build AND OPERATE ML products in production. It is a must-read for ML practitioners and Software Engineers Transitioning into ML.

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Python library for implementing Responsible AI mitigations.

With AutoBrewML Framework the time it takes to get production-ready ML models with great ease and efficiency highly accelerates.

Zero Trust AI 360

Open-source Project for Responsible Al Checklists in Machine Learning and Al Development

Introduction to responsible machine learning with Python

Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.

AAAI 2025 Tutorial on AI Safety

Responsible Prediction Making of COVID-19 Mortality (AAAI 2021)

Credit risk ML pipeline with CatBoost, SHAP & LIME explainability, fairness monitoring and auto generated PDF reports built for auditability over accuracy.

AI Equity Guide