slundberg / shap

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

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

Updated Oct 14, 2020
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EthicalML / awesome-production-machine-learning

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 Oct 15, 2020

interpretml / interpret

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Open

Interpret

4

wh3nd1g0h commented Jul 29, 2020

Yes

h1st-ai / h1st

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Human-First AI solves the “cold-start” problem of Industrial AI: encoding human expertise to augment the lack of data, while bridging to powerful ML—based on experience building AI solutions at Panasonic: robotics predictive maintenance, cold-chain energy optimization, Gigafactory battery mfg, avionics, automotive cybersecurity, and more.

home-automation data-science time-series collaboration cybersecurity cold-start autonomous-vehicles hacktoberfest automl avionics human-in-the-loop predictive-maintenance ensemble-machine-learning datascience-environment explainability industrial-iot trustworthy-datascience energy-optimization

Updated Oct 16, 2020
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EthicalML / xai

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XAI - An eXplainability toolbox for machine learning

machine-learning ai evaluation ml artificial-intelligence upsampling bias interpretability feature-importance explainable-ai explainable-ml xai imbalance downsampling explainability bias-evaluation machine-learning-explainability xai-library

Updated Oct 5, 2019
Python

MisaOgura / flashtorch

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Visualization toolkit for neural networks in PyTorch! Demo -->

visualization machine-learning deep-learning cnn pytorch neural-networks interpretability explainability

Updated Oct 5, 2020
Python

alvinwan / neural-backed-decision-trees

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Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

pytorch neural-networks imagenet image-classification pretrained-models decision-trees cifar10 interpretability pretrained-weights cifar100 tiny-imagenet explainability neural-backed-decision-trees

Updated Jun 17, 2020
Python

ModelOriented / modelStudio

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📍 Interactive Studio for Explanatory Model Analysis

machine-learning predictive-modeling interactive-visualizations interpretability explainable-artificial-intelligence explainable-ai explainable-ml xai model-visualization interpretable-machine-learning iml explainability explanatory-model-analysis explainable-machine-learning

Updated Sep 10, 2020
R

explainX / explainx

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Explain & debug blackbox machine learning models.

machine-learning scikit-learn transparency blackbox bias interpretability explainable-artificial-intelligence interpretable-ai explainable-ai explainable-ml xai interpretable-machine-learning machine-learning-interpretability explainability aws-sagemaker explainx

Updated Oct 7, 2020
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csinva / hierarchical-dnn-interpretations

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Using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions"

python data-science machine-learning statistics deep-neural-networks ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-networks acd interpretation interpretability feature-importance explainable-ai iclr2019 explainability

Updated Oct 3, 2020
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haofanwang / Score-CAM

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[CVPR 2020 Workshop] Official implementation of Score-CAM in Pytorch

heatmap grad-cam pytorch cam saliency class-activation-maps cnn-visualization-technique gradcam gradient-free cnn-visualization visual-explanations explainability score-cam scorecam

Updated Sep 26, 2020
Python

d909b / cxplain

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Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

machine-learning deep-learning interpretability explainable-ai explainable-ml explainability

Updated Aug 22, 2020
Python

baldassarreFe / graph-network-explainability

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Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

bioinformatics artificial-intelligence graph-networks explainability

Updated Nov 12, 2019
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laura-rieger / deep-explanation-penalization

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Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

python data-science machine-learning ai deep-learning neural-network jupyter-notebook ml pytorch artificial-intelligence convolutional-neural-network fairness interpretability cdep feature-importance recurrent-neural-network interpretable-deep-learning explainable-ai explainability fairness-ml

Updated Oct 5, 2020
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sebastian-hofstaetter / transformer-kernel-ranking

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TK & TKL - Efficient Transformer-based neural re-ranking models

efficiency transformer explainability neural-ir

Updated Jul 28, 2020
Python

SAP / contextual-ai

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Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.

machine-learning report-generator explainability

Updated Jul 28, 2020
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WHUIR / CARP

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The implementation of “A Capsule Network for Recommendation and Explaining What You Like and Dislike”, Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu, https://dl.acm.org/citation.cfm?doid=3331184.3331216

recommendation explainability caspule-network