Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Fit interpretable models. Explain blackbox machine learning.

Model interpretability and understanding for PyTorch

A curated list of awesome responsible machine learning resources.

A collection of research materials on explainable AI/ML

[CONTRIBUTORS WELCOME] Generalized Additive Models in Python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

Explain & debug any blackbox machine learning model with a single line of code.

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

PyTorch Explain: Interpretable Deep Learning in Python.

A curated list of awesome academic research, books, code of ethics, courses, databases, data sets, frameworks, institutes, maturity models, newsletters, principles, podcasts, regulations, reports, responsible scale policies, tools and standards related to Responsible, Trustworthy, and Human-Centered AI.

Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Explainable AI in Julia.

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

All about explainable AI, algorithmic fairness and more

Modular Python Toolbox for Fairness, Accountability and Transparency Forensics

This is an official implementation for PROMPT-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis (CVPR'25). Explore fine-grained trait distinctions between different specified species.