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.

This article was published as a part of the Data Science Blogathon. In the modern day, where there is a colossal amount of data at our disposal, using ML models to make decisions has become crucial in sectors like healthcare, finance, marketing, etc. Many ML models are black boxes since it is difficult to fully understand how they function after training. This makes it difficult to understand and explain a model's behaviour, but it is important to do so to have trust in its accuracy. So how can we build trust in the predictions of a black box?

TL;DR: OmniXAI (short for Omni eXplainable AI) is designed to address many of the pain points in explaining decisions made by AI models. This open-source library aims to provide data scientists, machine learning engineers, and researchers with a one-stop Explainable AI (XAI) solution to analyze, debug, and interpret their AI models for various data types in a wide range of tasks and applications. OmniXAI's powerful features and integrated framework make it a major addition to the burgeoning field of XAI. With the rapidly growing adoption of AI models in real-world applications, AI decision making can potentially have a huge societal impact, especially for application domains such as healthcare, education, and finance. However, many AI models, especially those based on deep neural networks, effectively work as black-box models that lack explainability.

Machine Learning models are frequently seen as black boxes that are impossible to decipher. Because the learner is trained to respond to "yes" and "no" type questions without explaining how the answer was obtained. An explanation of how an answer was achieved is critical in many applications for assuring confidence and openness. Explainable AI refers to strategies and procedures in the use of artificial intelligence technology (AI) that allow human specialists to understand the solution's findings. This article will focus on explaining the machine learner using OmniXAI.

OmniXAI (short for Omni eXplainable AI) is a Python machine-learning library for explainable AI (XAI), offering omni-way explainable AI and interpretable machine learning capabilities to address many pain points in explaining decisions made by machine learning models in practice. OmniXAI 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 explaination methods including "model-specific" and "model-agnostic" methods (such as feature-attribution explanation, counterfactual explanation, gradient-based explanation, etc). For practitioners, OmniXAI 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 for obtaining more insights about decisions. The following table shows the supported explanation methods and features in our library. We will continue improving this library to make it more comprehensive in the future, e.g., supporting more explanation methods for vision, NLP and time-series tasks.