  We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python
library of eXplainable AI (XAI), which offers omni-way explainable AI
capabilities and various interpretable machine learning techniques to address
the pain points of understanding and interpreting the decisions made by machine
learning (ML) in practice. OmniXAI aims to be a one-stop comprehensive library
that makes explainable AI easy for data scientists, ML researchers and
practitioners who need explanation for various types of data, models and
explanation methods at different stages of ML process (data exploration,
feature engineering, model development, evaluation, and decision-making, etc).
In particular, our library includes a rich family of explanation methods
integrated in a unified interface, which supports multiple data types (tabular
data, images, texts, time-series), multiple types of ML models (traditional ML
in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of
diverse explanation methods including "model-specific" and "model-agnostic"
ones (such as feature-attribution explanation, counterfactual explanation,
gradient-based explanation, etc). For practitioners, the library provides an
easy-to-use unified interface to generate the explanations for their
applications by only writing a few lines of codes, and also a GUI dashboard for
visualization of different explanations for more insights about decisions. In
this technical report, we present OmniXAI's design principles, system
architectures, and major functionalities, and also demonstrate several example
use cases across different types of data, tasks, and models.
