XAI-Lib

Welcome to the documentation of XAI-Lib - an integrated Python library for Explainable AI (XAI).

XAI-Lib provides a unified interface for various explanation methods, making machine learning models more interpretable and transparent. The library simplifies the process of explaining black-box models across different data types.

Note

This project is part of the XAI Project - a European initiative focused on advancing explainable artificial intelligence research and applications.

About This Documentation

This documentation is formatted in reStructuredText and built with Sphinx. It provides comprehensive information about:

• Installation and getting started

• Available explanation methods

• API reference

• Usage examples

• Contributing guidelines

The documentation uses the autodoc extension to include documentation from docstrings, which are written in Google style.

Contents

• Overview
  • ✨ Features
  • 🛠️ Supported Explanation Methods
  • 📦 Installation
  • 🚀 Quick Start
  • 📖 Documentation
  • 🤝 Contributing
  • 👥 Authors & Contributors
  • 📄 License
  • Acknowledgments
  • 📧 Contact
• Contributions & Help
  • Issue Reports
  • Documentation Improvements
  • Code Contributions
  • Maintainer tasks
• License
• Authors
• Changelog
  • Version 0.1
• Module Reference
  • xailib package

Indices and tables

• Index

• Module Index

• Search Page

Acknowledgments

This library is developed as part of the XAI Project (https://xai-project.eu/), a European initiative dedicated to advancing explainable artificial intelligence.

The XAI Project aims to:

• Develop new methods for explainable AI

• Create practical tools for AI transparency

• Foster collaboration between research and industry

• Promote responsible AI development

For more information about the XAI Project, visit https://xai-project.eu/.