OpenXAI FAQs

Question 1

How does OpenXAI help evaluate explanation quality?

Accepted Answer

It offers diverse evaluation metrics across categories like faithfulness (e.g., PGI, PGU), stability (e.g., RIS, RRS), ground truth, and fairness (e.g., Subgroup analysis) to thoroughly assess explanation reliability.

Question 2

What is OpenXAI?

Accepted Answer

OpenXAI is a lightweight library and Model Context Protocol (MCP) server that provides comprehensive tools for systematically evaluating and benchmarking post hoc explanation methods in AI models through a standard interface.

Question 3

Which AI explanation methods does OpenXAI support?

Accepted Answer

OpenXAI supports a wide array of popular explanation methods, including LIME, SHAP, Integrated Gradients, Grad-CAM, and Guided Backpropagation, enabling broad analysis.

Question 4

Can OpenXAI be used for benchmarking AI models?

Accepted Answer

Yes, OpenXAI provides access to public XAI benchmarking results and leaderboards. This allows users to transparently compare the performance and reliability of various explanation methods.

Question 5

What types of data and models can I use with OpenXAI?

Accepted Answer

OpenXAI includes synthetic and real-world datasets for tabular, image, and text modalities. It also offers pre-trained models such as ANN, Logistic Regression, Random Forest, SVM, and XGBoost for immediate testing and evaluation.

OpenXAI

OpenXAI

Key Features

Use Cases

Key Features

Use Cases