# OmniXAI: A Library for Explainable AI

arXiv: https://arxiv.org/abs/2206.01612 · HTML: https://mlsanity.com/paper/2206.01612/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 28 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://mlsanity.com/paper/1802.07814/) (2018)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://mlsanity.com/paper/2101.00288/) (2021)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation](https://mlsanity.com/paper/2003.03934/) (2020)
- [dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python](https://mlsanity.com/paper/2012.14406/) (2020)
- [MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation](https://mlsanity.com/paper/2205.15540/) (2022)
- [Proposed Guidelines for the Responsible Use of Explainable Machine Learning](https://mlsanity.com/paper/1906.03533/) (2019)

## Built on by (most cited first, 21 of 21)

- [Which LIME should I trust? Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2503.24365/) (2025)
- [TSInterpret: A unified framework for time series interpretability](https://mlsanity.com/paper/2208.05280/) (2022)
- [Time Interpret: a Unified Model Interpretability Library for Time Series](https://mlsanity.com/paper/2306.02968/) (2023)
- [Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods](https://mlsanity.com/paper/2505.01198/) (2025)
- [OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System](https://mlsanity.com/paper/2303.00501/) (2023)
- [An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services](https://mlsanity.com/paper/2411.03376/) (2024)
- [CLIMAX: An exploration of Classifier-Based Contrastive Explanations](https://mlsanity.com/paper/2307.00680/) (2023)
- [Cloud-based XAI Services for Assessing Open Repository Models Under Adversarial Attacks](https://mlsanity.com/paper/2401.12261/) (2024)
- [EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications](https://mlsanity.com/paper/2403.18203/) (2024)
- [Inserting Faces inside Captions: Image Captioning with Attention Guided Merging](https://mlsanity.com/paper/2405.02305/) (2024)
- [PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models](https://mlsanity.com/paper/2505.10515/) (2025)
- [The XAISuite framework and the implications of explanatory system dissonance](https://mlsanity.com/paper/2304.08499/) (2023)
- [ONNXExplainer: an ONNX Based Generic Framework to Explain Neural Networks Using Shapley Values](https://mlsanity.com/paper/2309.16916/) (2023)
- [A novel post-hoc explanation comparison metric and applications](https://mlsanity.com/paper/2311.10811/) (2023)
- [NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques](https://mlsanity.com/paper/2401.17200/) (2024)
- [Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI](https://mlsanity.com/paper/2407.12950/) (2024)
- [Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense](https://mlsanity.com/paper/2505.03424/) (2025)
- [ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability](https://mlsanity.com/paper/2505.06258/) (2025)
- [ACE: Adapting sampling for Counterfactual Explanations](https://mlsanity.com/paper/2509.26322/) (2025)
- [ExECG: An Explainable AI Framework for ECG models](https://mlsanity.com/paper/2605.19258/) (2026)
- [Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance](https://mlsanity.com/paper/2608.10434/) (2026)

## Abstract (arXiv)

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.