shap / shap

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

EthicalML / awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

interpretml / interpret

Fit interpretable models. Explain blackbox machine learning.

MAIF / shapash

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

CodeBoarding / CodeBoarding

Interactive architecture diagrams for codebases

microsoft / responsible-ai-toolbox

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

alexgreensh / attention-span

Make your agents talk human. ADHD-friendly output styles for Claude Code, Codex, and others. So you can pay attention, not tokens.

mmschlk / shapiq

Shapley Interactions and Shapley Values for Machine Learning

deel-ai / xplique

👋 Xplique is a Neural Networks Explainability Toolbox

vincenzo-afk / TruthNet

AI-powered fake news detection system with multiple ML models and explainability.