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Awesome-explainable-AI
A collection of research materials on explainable AI/ML
https://github.com/wangyongjie-ntu/Awesome-explainable-AI
Last synced: 3 days ago
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Survey Papers
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
- Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
- Explainable Biometrics in the Age of Deep Learning
- A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods
- Interpretable machine learning:Fundamental principles and 10 grand challenges
- Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing
- Pitfalls of Explainable ML: An Industry Perspective
- The elephant in the interpretability room: Why use attention as explanation when we have saliency methods
- A Survey of the State of Explainable AI for Natural Language Processing - IJCNLP 2020
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- A brief survey of visualization methods for deep learning models from the perspective of Explainable AI
- Explaining Explanations in AI
- Machine learning interpretability: A survey on methods and metrics
- A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
- Interpretable machine learning: definitions, methods, and applications
- Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
- Explainable Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
- Explanation in artificial intelligence: Insights from the social sciences
- Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
- Explanation in Human-AI Systems: A Literature Meta-Review Synopsis of Key Ideas and Publications and Bibliography for Explainable AI
- Explaining Explanations: An Overview of Interpretability of Machine Learning
- Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
- Explainable artificial intelligence: A survey
- The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery
- Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
- Towards A Rigorous Science of Interpretable Machine Learning
- Explaining Explanation, Part 1: Theoretical Foundations
- Explaining Explanation, Part 2: Empirical Foundations
- Explaining Explanation, Part 3: The Causal Landscape
- Explaining Explanation, Part 4: A Deep Dive on Deep Nets
- An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data
- Review and comparison of methods to study the contribution of variables in artificial neural network models
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Explanation in artificial intelligence: Insights from the social sciences
- An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Towards A Rigorous Science of Interpretable Machine Learning
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
- Explainable artificial intelligence: A survey
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- How Convolutional Neural Networks See the World — A Survey of Convolutional Neural Network Visualization Methods
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
- From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI - dmb.github.io/xai-papers/)
- Pitfalls of Explainable ML: An Industry Perspective
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Explaining Explanations in AI
- Interpretable machine learning: definitions, methods, and applications
- Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
- Explanation in Human-AI Systems: A Literature Meta-Review Synopsis of Key Ideas and Publications and Bibliography for Explainable AI
- How Convolutional Neural Networks See the World — A Survey of Convolutional Neural Network Visualization Methods
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
- Benchmarking and Survey of Explanation Methods for Black Box Models
- Post-hoc Interpretability for Neural NLP: A Survey
- Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges
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Books
- Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models
- Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
- Explanation in Artificial Intelligence: Insights from the Social Sciences
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Explanatory Model Analysis Explore, Explain and Examine Predictive Models
- Limitations of Interpretable Machine Learning Methods
- An Introduction to Machine Learning Interpretability An Applied Perspective on Fairness, Accountability, Transparency,and Explainable AI
- Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Explanatory Model Analysis Explore, Explain and Examine Predictive Models
- An Introduction to Machine Learning Interpretability An Applied Perspective on Fairness, Accountability, Transparency,and Explainable AI
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Interpretable Machine Learning A Guide for Making Black Box Models Explainable
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Explanatory Model Analysis Explore, Explain and Examine Predictive Models
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- An Introduction to Machine Learning Interpretability An Applied Perspective on Fairness, Accountability, Transparency,and Explainable AI
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Explanation in Artificial Intelligence: Insights from the Social Sciences
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
- Visualizations of Deep Neural Networks in Computer Vision: A Survey
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Open Courses
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Papers
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Evaluation methods
- Faithfulness Tests for Natural Language Explanations
- OpenXAI: Towards a Transparent Evaluation of Model Explanations
- When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data
- Towards Better Understanding Attribution Methods
- What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors
- Evaluations and Methods for Explanation through Robustness Analysis
- Evaluating and Aggregating Feature-based Model Explanations
- Sanity Checks for Saliency Metrics
- A benchmark for interpretability methods in deep neural networks
- What Do Different Evaluation Metrics Tell Us About Saliency Models?
- Methods for interpreting and understanding deep neural networks
- Evaluating the visualization of what a Deep Neural Network has learned
- Sanity Checks for Saliency Metrics
- A benchmark for interpretability methods in deep neural networks
- From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI - dmb.github.io/xai-papers/)
- OpenXAI: Towards a Transparent Evaluation of Model Explanations
- What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors
- Evaluations and Methods for Explanation through Robustness Analysis
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Python Libraries(sort in alphabeta order)
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Evaluation methods
- https://github.com/IBM/AIX360
- https://github.com/SeldonIO/alibi-detect - detect?style=social)
- https://github.com/BCG-Gamma/facet - Gamma/facet?style=social)
- https://github.com/alvinwan/neural-backed-decision-trees - backed-decision-trees?style=social)
- https://github.com/utkuozbulak/pytorch-cnn-visualizations - cnn-visualizations?style=social)
- https://github.com/slundberg/shap
- https://github.com/oracle/Skater
- https://github.com/microsoft/tensorwatch.git
- https://github.com/deel-ai/xplique - ai/xplique?style=social)
- https://github.com/IBM/AIX360
- https://github.com/BCG-Gamma/facet - Gamma/facet?style=social)
- https://github.com/TimKam/py-ciu/ - ciu/?style=social)
- https://github.com/slundberg/shap
- https://github.com/microsoft/tensorwatch.git
- https://github.com/IBM/AIX360
- https://github.com/Trusted-AI/AIF360 - AI/AIF360.svg?style=social)
- https://github.com/marcotcr/anchor - learn 
- https://github.com/SeldonIO/alibi
- https://github.com/SeldonIO/alibi-detect - detect?style=social)
- https://github.com/algofairness/BlackBoxAuditing - learn 
- https://github.com/vkola-lab/brain2020 - lab/brain2020?style=social)
- https://github.com/Ekeany/Boruta-Shap - learn 
- https://github.com/kondiz/casme
- https://github.com/pytorch/captum
- https://github.com/idealo/cnn-exposed - exposed?style=social)
- https://github.com/wilsonjr/ClusterShapley
- https://github.com/ModelOriented/DALEX
- https://github.com/kundajelab/deeplift
- https://github.com/marcoancona/DeepExplain
- https://github.com/yosinski/deep-visualization-toolbox - visualization-toolbox?style=social)
- https://github.com/dianna-ai/dianna - ai/dianna?style=social)
- https://github.com/TeamHG-Memex/eli5 - learn, Keras, xgboost, lightGBM, catboost etc.
- https://github.com/MarcelRobeer/explabox - learn, Pytorch, Keras, Tensorflow, Huggingface 
- https://github.com/explainX/explainx
- https://github.com/neulab/ExplainaBoard
- https://github.com/navefr/ExKMC
- https://github.com/insikk/Grad-CAM-tensorflow - CAM-tensorflow?style=social)
- https://github.com/lethaiq/GRACE_KDD20
- https://github.com/albermax/innvestigate
- https://github.com/csinva/imodels
- https://github.com/interpretml/interpret
- https://github.com/interpretml/interpret-community - community.svg?style=social)
- https://github.com/ankurtaly/Integrated-Gradients - Gradients?style=social)
- https://github.com/jacobgil/keras-grad-cam - grad-cam?style=social)
- https://github.com/raghakot/keras-vis - vis?style=social)
- https://github.com/philipperemy/keract
- https://github.com/tensorflow/lucid
- https://github.com/PAIR-code/lit - code/lit?style=social)
- https://github.com/marcotcr/lime
- https://github.com/aerdem4/lofo-importance - learn 
- https://github.com/ModelOriented/modelStudio
- https://github.com/MECLabTUDA/M3d-Cam - Cam?style=social)
- https://github.com/fdalvi/NeuroX
- https://github.com/alvinwan/neural-backed-decision-trees - backed-decision-trees?style=social)
- https://github.com/david-cortes/outliertree - cortes/outliertree?style=social)
- https://github.com/PaddlePaddle/InterpretDL
- https://github.com/tongshuangwu/polyjuice
- https://github.com/utkuozbulak/pytorch-cnn-visualizations - cnn-visualizations?style=social)
- https://github.com/jacobgil/pytorch-grad-cam - grad-cam?style=social)
- https://github.com/SauceCat/PDPbox - learn 
- https://github.com/TimKam/py-ciu/ - ciu/?style=social)
- https://github.com/AustinRochford/PyCEbox
- https://github.com/suinleelab/path_explain
- https://github.com/understandable-machine-intelligence-lab/Quantus - machine-intelligence-lab/Quantus?style=social)
- https://github.com/christophM/rulefit
- https://github.com/rulematrix/rule-matrix-py - matrix-py?style=social)
- https://github.com/PAIR-code/saliency - code/saliency?style=social)
- https://github.com/slundberg/shap
- https://github.com/benedekrozemberczki/shapley
- https://github.com/tensorflow/tcav - learn 
- https://github.com/scikit-learn-contrib/skope-rules - learn 
- https://github.com/sicara/tf-explain - explain?style=social)
- https://github.com/andosa/treeinterpreter - learn, 
- https://github.com/frgfm/torch-cam - cam?style=social)
- https://github.com/CalculatedContent/WeightWatcher
- https://github.com/PAIR-code/what-if-tool - code/what-if-tool?style=social)
- https://github.com/EthicalML/xai - learn 
- https://github.com/deel-ai/xplique - ai/xplique?style=social)
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Stargazers over time
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Evaluation methods
- ![Stargazers over time - ntu/Awesome-explainable-AI)
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Related Repositories
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Evaluation methods
- https://github.com/jphall663/awesome-machine-learning-interpretability - machine-learning-interpretability?style=social)
- https://github.com/lopusz/awesome-interpretable-machine-learning - interpretable-machine-learning?style=social)
- https://github.com/pbiecek/xai_resources
- https://github.com/h2oai/mli-resources - resources?style=social)
- https://github.com/AstraZeneca/awesome-explainable-graph-reasoning - explainable-graph-reasoning?style=social)
- https://github.com/utwente-dmb/xai-papers - dmb/xai-papers?style=social)
- https://github.com/samzabdiel/XAI
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Programming Languages
Categories
Sub Categories
Keywords
machine-learning
30
interpretability
22
explainable-ai
21
explainable-ml
16
deep-learning
14
xai
14
interpretable-machine-learning
11
python
9
interpretable-ai
7
data-science
7
visualization
7
pytorch
7
grad-cam
6
ai
6
explainability
5
interpretable-ml
5
tensorflow
5
fairness
5
artificial-intelligence
5
explainable-artificial-intelligence
4
iml
4
bias
4
explanations
4
interpretable-deep-learning
4
transparency
4
scikit-learn
4
saliency-map
3
r
3
keras
3
convolutional-neural-networks
3
smoothgrad
3
visualizations
3
feature-importance
3
machine-learning-interpretability
3
ml
3
score-cam
2
object-detection
2
trusted-ai
2
ibm-research
2
saliency
2
explanatory-model-analysis
2
model-visualization
2
ibm-research-ai
2
responsible-ai
2
blackbox
2
guided-backpropagation
2
xgboost
2
image-classification
2
nlp
2
codait
2