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interpretability

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shap / shap

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A game theoretic approach to explain the output of any machine learning model.

machine-learning deep-learning gradient-boosting interpretability shapley shap explainability

Updated Aug 11, 2026
Jupyter Notebook

EthicalML / awesome-production-machine-learning

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A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

machine-learning data-mining awesome deep-learning awesome-list interpretability privacy-preserving production-machine-learning mlops privacy-preserving-machine-learning explainability responsible-ai machine-learning-operations ml-ops ml-operations privacy-preserving-ml large-scale-ml production-ml large-scale-machine-learning

Updated Aug 12, 2026

jacobgil / pytorch-grad-cam

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

machine-learning computer-vision deep-learning grad-cam pytorch image-classification object-detection visualizations interpretability class-activation-maps interpretable-deep-learning interpretable-ai explainable-ai explainable-ml xai score-cam vision-transformers

Updated Aug 13, 2026
Python

interpretml / interpret

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Fit interpretable models. Explain blackbox machine learning.

machine-learning ai scikit-learn artificial-intelligence transparency blackbox bias differential-privacy gradient-boosting interpretability interpretable-ai interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning iml explainability interpretml

Updated Aug 17, 2026
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meta-pytorch / captum

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Model interpretability and understanding for PyTorch

interpretability feature-importance interpretable-ai interpretable-ml feature-attribution

Updated Aug 19, 2026
Python

tensorflow / lucid

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A collection of infrastructure and tools for research in neural network interpretability.

visualization machine-learning tensorflow jupyter-notebook colab interpretability

Updated Feb 6, 2023
Jupyter Notebook

jphall663 / awesome-machine-learning-interpretability

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A curated list of awesome responsible machine learning resources.

Updated Jun 3, 2026

MAIF / shapash

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🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

python machine-learning transparency lime interpretability ethical-artificial-intelligence explainable-ml shap explainability

Updated Jul 28, 2026
Jupyter Notebook

stellargraph / stellargraph

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StellarGraph - Machine Learning on Graphs

python data-science machine-learning deep-learning graphs machine-learning-algorithms networkx graph-data graph-analysis graph-machine-learning link-prediction graph-convolutional-networks gcn saliency-map interpretability geometric-deep-learning graph-neural-networks heterogeneous-networks stellargraph-library

Updated Apr 10, 2024
Python

SeldonIO / alibi

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Algorithms for explaining machine learning models

machine-learning interpretability counterfactual explanations xai

Updated Oct 17, 2025
Python

frgfm / torch-cam

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Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM, Finer-CAM, LeGrad, RefineCAM)

python deep-learning grad-cam cnn pytorch saliency-map interpretability smoothgrad interpretable-deep-learning gradcam activation-maps class-activation-map gradcam-plus-plus score-cam

Updated Aug 18, 2026
Python

chaoyanghe / Awesome-Federated-Learning

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FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Updated Sep 3, 2022

google-deepmind / penzai

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A JAX research toolkit for building, editing, and visualizing neural networks.

visualization neural-networks interpretability fine-tuning jax

Updated Jun 22, 2025
Python

microsoft / responsible-ai-toolbox

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Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.