OpenXAI
Provides comprehensive tools for evaluating and benchmarking post hoc explanation methods in AI models through a standard interface.
About
This tool serves as a Model Context Protocol (MCP) server for OpenXAI, a robust and lightweight library dedicated to systematically evaluating the reliability of post hoc explanation methods in artificial intelligence. It exposes OpenXAI's extensive capabilities—including a wide array of explanation methods like LIME and SHAP, diverse evaluation metrics for faithfulness and stability, various datasets (tabular, image, text), and pre-trained models—via a standardized interface. This enables seamless integration with AI assistants and other MCP-compatible applications, facilitating transparent understanding and rigorous benchmarking of AI model behaviors.
Key Features
- Supports a wide range of explanation methods (LIME, SHAP, Integrated Gradients, Grad-CAM, Guided Backpropagation).
- Includes diverse evaluation metrics for faithfulness, stability, ground truth, and fairness.
- Provides access to synthetic and real-world datasets across tabular, image, and text modalities.
- Offers pre-trained models (ANN, Logistic Regression, Random Forest, SVM, XGBoost) for testing.
- Access to public XAI benchmarking results and leaderboards for method comparison.
- 0 GitHub stars
Use Cases
- Generating and evaluating explanations for AI model predictions using various methods and metrics.
- Benchmarking and comparing the performance of different AI explanation methods across datasets.
- Deploying and managing AI models with integrated explanation and monitoring capabilities via various cloud providers or decentralized options.
