microsoft/responsible-ai-toolbox

A Python package to assess and improve fairness of machine learning models.

Interpretability and explainability of data and machine learning models

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.

A game theoretic approach to explain the output of any machine learning model.

AI Observability & Evaluation

Laminar - open-source observability platform purpose-built for AI agents. YC S24.

Fit interpretable models. Explain blackbox machine learning.