This repository contains the frontier research on explainable AI(XAI) which is a hot topic recently. From the figure below we can see the trend of interpretable/explainable AI. The publications on this topic are booming.
The figure below illustrates several use cases of XAI. Here we also divide the publications into serveal categories based on this figure. It is challenging to organise these papers well. Good to hear your voice!
Benchmarking and Survey of Explanation Methods for Black Box Models, Arxiv preprint 2021
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods, EMNLP Workshop 2020
Explainable Machine Learning in Deployment, FAT 2020
Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges, Communications in Computer and Information Science 2020
A brief survey of visualization methods for deep learning models from the perspective of Explainable AI, Information Visualization 2020
Explaining Explanations in AI, ACM FAT 2019
Machine learning interpretability: A survey on methods and metrics, Electronics, 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI, IEEE TNNLS 2020
Interpretable machine learning: definitions, methods, and applications, Arxiv preprint 2019
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers, IEEE Transactions on Visualization and Computer Graphics, 2019
Explainable Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Information Fusion, 2019
Explanation in artificial intelligence: Insights from the social sciences, Artificial Intelligence 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning, Arxiv preprint 2019
A survey of methods for explaining black box models, ACM Computing Surveys, 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning, IEEE DSAA, 2018
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI), IEEE Access, 2018
Explainable artificial intelligence: A survey, MIPRO, 2018
The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery, ACM Queue 2018
[What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors How Convolutional Neural Networks See the World — A Survey of Convolutional Neural Network Visualization Methods, Mathematical Foundations of Computing 2018
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models, Arxiv 2017
Towards A Rigorous Science of Interpretable Machine Learning, Arxiv preprint 2017
Explaining Explanation, Part 1: Theoretical Foundations, IEEE Intelligent System 2017
Explaining Explanation, Part 2: Empirical Foundations, IEEE Intelligent System 2017
Explaining Explanation, Part 3: The Causal Landscape, IEEE Intelligent System 2017
Explaining Explanation, Part 4: A Deep Dive on Deep Nets, IEEE Intelligent System 2017
An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data, Ecological Modelling 2004
Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecological Modelling 2003
Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models, Advances in Deep Learning Chapter 2020
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer 2019
Explanation in Artificial Intelligence: Insights from the Social Sciences, 2017 arxiv preprint
Visualizations of Deep Neural Networks in Computer Vision: A Survey, Springer Transparent Data Mining for Big and Small Data 2017
Explanatory Model Analysis Explore, Explain and Examine Predictive Models
Interpretable Machine Learning A Guide for Making Black Box Models Explainable
Limitations of Interpretable Machine Learning Methods
Interpretability and Explainability in Machine Learning, Harvard University
We mainly follow the taxonomy in the survey paper and divide the XAI/XML papers into the several branches.
Learning Groupwise Explanations for Black-Box Models, IJCAI 2021
On Explaining Random Forests with SAT, IJCAI 2021
What Changed? Interpretable Model Comparison, IJCAI 2021
Towards Probabilistic Sufficient Explanations, IJCAI 2021
On Explainability of Graph Neural Networks via Subgraph Explorations, ICML 2021
Why Attentions May Not Be Interpretable?, KDD 2021
Where and What? Examining Interpretable Disentangled Representations, CVPR 2021
Have We Learned to Explain?, AISTATS 2021
Does Explainable Artificial Intelligence Improve Human Decision-Making?, AAAI 2021
Incorporating Interpretable Output Constraints in Bayesian Neural Networks, NeuIPS 2020
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables, NeurIPS 2020
Learning Deep Attribution Priors Based On Prior Knowledge, NeurIPS 2020
Understanding Global Feature Contributions through Additive Importance Measures, NeurIPS 2020
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE, NeurIPS 2020
Generative causal explanations of black-box classifiers, NeurIPS 2020
Learning outside the Black-Box: The pursuit of interpretable models, NeurIPS 2020
Explaining Groups of Points in Low-Dimensional Representations, ICML 2020
Explaining Knowledge Distillation by Quantifying the Knowledge, CVPR 2020
Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned Systems, IJCAI 2020
Machine Learning Explainability for External Stakeholders, IJCAI 2020
Py-CIU: A Python Library for Explaining Machine Learning Predictions Using Contextual Importance and Utility, IJCAI 2020
Machine Learning Explainability for External Stakeholders, IJCAI 2020
Interpretable Models for Understanding Immersive Simulations, IJCAI 2020
Towards Automatic Concept-based Explanations, NIPS 2019
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead, Nature Machine Intelligence 2019
Interpretml: A unified framework for machine learning interpretability, arxiv preprint 2019
On the Robustness of Interpretability Methods, ICML 2018 workshop
Towards A Rigorous Science of Interpretable Machine Learning, Arxiv preprint 2017
LOCO, Distribution-Free Predictive Inference For Regression, Arxiv preprint 2016
Explaining data-driven document classifications, MIS Quarterly 2014
Evaluations and Methods for Explanation through Robustness Analysis, arxiv preprint 2020
Evaluating and Aggregating Feature-based Model Explanations, IJCAI 2020
Sanity Checks for Saliency Metrics, AAAI 2020
A benchmark for interpretability methods in deep neural networks, NIPS 2019
Methods for interpreting and understanding deep neural networks, Digital Signal Processing 2017
Evaluating the visualization of what a Deep Neural Network has learned, IEEE Transactions on Neural Networks and Learning Systems 2015
AIF360: https://github.com/Trusted-AI/AIF360,
AIX360: https://github.com/IBM/AIX360,
Anchor: https://github.com/marcotcr/anchor, scikit-learn
Alibi: https://github.com/SeldonIO/alibi
Alibi-detect: https://github.com/SeldonIO/alibi-detect
BlackBoxAuditing: https://github.com/algofairness/BlackBoxAuditing, scikit-learn
Brain2020: https://github.com/vkola-lab/brain2020, Pytorch, 3D Brain MRI
Boruta-Shap: https://github.com/Ekeany/Boruta-Shap, scikit-learn
casme: https://github.com/kondiz/casme, Pytorch
Captum: https://github.com/pytorch/captum, Pytorch,
cnn-exposed: https://github.com/idealo/cnn-exposed, Tensorflow
ClusterShapley: https://github.com/wilsonjr/ClusterShapley, Sklearn
DALEX: https://github.com/ModelOriented/DALEX,
Deeplift: https://github.com/kundajelab/deeplift, Tensorflow, Keras
DeepExplain: https://github.com/marcoancona/DeepExplain, Tensorflow, Keras
Deep Visualization Toolbox: https://github.com/yosinski/deep-visualization-toolbox, Caffe,
Eli5: https://github.com/TeamHG-Memex/eli5, Scikit-learn, Keras, xgboost, lightGBM, catboost etc.
explainx: https://github.com/explainX/explainx, xgboost, catboost
ExplainaBoard: https://github.com/neulab/ExplainaBoard,
Facet: https://github.com/BCG-Gamma/facet, sklearn,
Grad-cam-Tensorflow: https://github.com/insikk/Grad-CAM-tensorflow, Tensorflow
GRACE: https://github.com/lethaiq/GRACE_KDD20, Pytorch
Innvestigate: https://github.com/albermax/innvestigate, tensorflow, theano, cntk, Keras
imodels: https://github.com/csinva/imodels,
InterpretML: https://github.com/interpretml/interpret
interpret-community: https://github.com/interpretml/interpret-community
Integrated-Gradients: https://github.com/ankurtaly/Integrated-Gradients, Tensorflow
Keras-grad-cam: https://github.com/jacobgil/keras-grad-cam, Keras
Keras-vis: https://github.com/raghakot/keras-vis, Keras
keract: https://github.com/philipperemy/keract, Keras
Lucid: https://github.com/tensorflow/lucid, Tensorflow
LIT: https://github.com/PAIR-code/lit, Tensorflow, specified for NLP Task
Lime: https://github.com/marcotcr/lime, Nearly all platform on Python
LOFO: https://github.com/aerdem4/lofo-importance, scikit-learn
modelStudio: https://github.com/ModelOriented/modelStudio, Keras, Tensorflow, xgboost, lightgbm, h2o
M3d-Cam: https://github.com/MECLabTUDA/M3d-Cam, PyTorch,
neural-backed-decision-trees: https://github.com/alvinwan/neural-backed-decision-trees, Pytorch
pytorch-cnn-visualizations: https://github.com/utkuozbulak/pytorch-cnn-visualizations, Pytorch
Pytorch-grad-cam: https://github.com/jacobgil/pytorch-grad-cam, Pytorch
PDPbox: https://github.com/SauceCat/PDPbox, Scikit-learn
py-ciu:https://github.com/TimKam/py-ciu/,
PyCEbox: https://github.com/AustinRochford/PyCEbox
path_explain: https://github.com/suinleelab/path_explain, Tensorflow
rulefit: https://github.com/christophM/rulefit,
rulematrix: https://github.com/rulematrix/rule-matrix-py,
Saliency: https://github.com/PAIR-code/saliency, Tensorflow
SHAP: https://github.com/slundberg/shap, Nearly all platform on Python
Shapley: https://github.com/benedekrozemberczki/shapley,
Skater: https://github.com/oracle/Skater
TCAV: https://github.com/tensorflow/tcav, Tensorflow, scikit-learn
skope-rules: https://github.com/scikit-learn-contrib/skope-rules, Scikit-learn
TensorWatch: https://github.com/microsoft/tensorwatch.git, Tensorflow
tf-explain: https://github.com/sicara/tf-explain, Tensorflow
Treeinterpreter: https://github.com/andosa/treeinterpreter, scikit-learn,
torch-cam: https://github.com/frgfm/torch-cam, Pytorch,
WeightWatcher: https://github.com/CalculatedContent/WeightWatcher, Keras, Pytorch
What-if-tool: https://github.com/PAIR-code/what-if-tool, Tensorflow
XAI: https://github.com/EthicalML/xai, scikit-learn
https://github.com/jphall663/awesome-machine-learning-interpretability,
https://github.com/lopusz/awesome-interpretable-machine-learning,
https://github.com/pbiecek/xai_resources,
https://github.com/h2oai/mli-resources,
https://github.com/AstraZeneca/awesome-explainable-graph-reasoning,
Need your help to re-organize and refine current taxonomy. Thanks very very much!
I appreciate it very much if you could add more works related to XAI/XML to this repo, archive uncategoried papers or anything to enrich this repo.
If any questions, feel free to contact me(yongjie.wang@ntu.edu.sg) or discuss on Gitter Chat. Welcome to discuss together.
