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#

interpretable-ai

Here are 138 public repositories matching this topic...

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

  • UpdatedApr 7, 2025
  • Python

Model interpretability and understanding for PyTorch

  • UpdatedDec 16, 2025
  • Python

A collection of research materials on explainable AI/ML

  • UpdatedDec 11, 2025
  • Markdown
pyGAM

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

  • UpdatedJun 17, 2024
  • Jupyter Notebook
explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu

  • UpdatedAug 21, 2024
  • Jupyter Notebook

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

  • UpdatedJul 31, 2025
  • Python
Machine-Learning

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

  • UpdatedJun 22, 2025
  • Python

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.

  • UpdatedDec 8, 2022
  • Jupyter Notebook

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"

  • UpdatedMar 12, 2024
  • 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.

  • UpdatedDec 17, 2025

Modular Python Toolbox for Fairness, Accountability and Transparency Forensics

  • UpdatedJun 9, 2023
  • Python

Python library to explain Tree Ensemble models (TE) like XGBoost, using a rule list.

  • UpdatedApr 22, 2024
  • Python

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