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

python data-science machine-learning data-mining h2o gradient-boosting-machine transparency decision-tree fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability

Updated Feb 10, 2021
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h2oai / mli-resources

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H2O.ai Machine Learning Interpretability Resources

python data-science machine-learning data-mining h2o xgboost transparency jupyter-notebooks fairness accountability interpretability interpretable-ai interpretable-ml explainable-ml mli xai fatml interpretable-machine-learning iml machine-learning-interpretability

Updated Dec 12, 2020
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sergioburdisso / pyss3

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A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI

)

nlp machine-learning natural-language-processing text-mining data-mining text-classification machine-learning-algorithms artificial-intelligence document-classification sentence-classification interpretability multilabel-classification explainable-artificial-intelligence interpretable-ml xai interpretable-machine-learning document-categorization early-classification text-labeling ss3-classifier

Updated Apr 4, 2022
Python

12wang3 / rrl

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The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".

scalability representation-learning nips interpretability interpretable-ai interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning iml explainability neurips interpretable-classification rule-based-model neuro-symbolic-ai

Updated Feb 9, 2022
Python

chr5tphr / zennit

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

python machine-learning deep-learning attribution pytorch interpretability interpretable-ai interpretable-ml explainable-ai xai lrp explainability feature-attribution

Updated May 2, 2022
Python

jphall663 / diabetes_use_case

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Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

python data-science machine-learning data-mining healthcare xgboost transparency interpretability interpretable-ml explainable-ml xai interpretable-machine-learning iml machine-learning-interpretability

Updated Nov 12, 2020
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jphall663 / hc_ml

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Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

data-science machine-learning data-mining transparency fairness accountability interpretability interpretable-ai interpretable-ml explainable-ai explainable-ml xai fatml interpretable-machine-learning iml machine-learning-interpretability fairness-ai fairness-ml

Updated Nov 19, 2019
TeX

navdeep-G / interpretable-ml

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Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

python data-science machine-learning data-mining gradient-boosting-machine transparency decision-trees fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability

Updated Mar 12, 2022
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12wang3 / mllp

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The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

machine-learning transparency aaai interpretability rule-based interpretable-ai interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning iml machine-learning-interpretability explainability rule-sets interpretml transparent-ml

Updated Dec 3, 2021
Python

h2oai / article-information-2019

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Article for Special Edition of Information: Machine Learning with Python

python data-science machine-learning interpretable-ai interpretable-ml explainable-ai explainable-ml xai fatml fairness-testing interpretable-machine-learning iml machine-learning-interpretability fairness-ai fairness-ml

Updated May 6, 2020
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ben-ix / XAI

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Genetic programming method for explaining complex black-box models

interpretable-deep-learning interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning

Updated Sep 30, 2019
Python

pnxenopoulos / cav-keras

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Concept activation vectors for Keras

interpretability explainable-artificial-intelligence interpretable-deep-learning interpretable-ai interpretable-ml explainable-ai explainable-ml interpretable-machine-learning explainability

Updated Feb 10, 2022
Python

slds-lmu / imlplots

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Create Interpretable Machine Learning plots with an interactive Shiny based dashboard

shinydashboard interpretable-ml

Updated Sep 19, 2018
R

schwettmann / visual-vocab

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Pytorch-based tools for constructing a vocabulary of visual concepts in a GAN.

deep-learning pytorch gan human-computer-interaction interpretable-ml

Updated Feb 25, 2022
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jphall663 / jsm_2018_paper

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Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539

python data-science machine-learning data-mining transparency interpretability interpretable-ai interpretable-ml explainable-ml xai fatml interpretable-machine-learning iml machine-learning-interpretability

Updated Dec 7, 2018
TeX

BirkhoffG / Explainable-ML-Papers

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A list of research papers of explainable machine learning.

machine-learning awesome research paper academic survey human-in-the-loop interpretability human-in-the-loop-machine-learning explanations interpretable-ml explainable-ml xai explainability counterfactual-explanations interpretable-models recourse trustworthy-machine-learning human-ai-interaction

Updated Jun 25, 2021

CeciPani / MARLENA

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A python library to agnostically explain multi-label black-box classifiers (tabular data)

python3 explanation interpretability multilabel multilabel-classification explainable-artificial-intelligence interpretable-ai interpretable-ml explainable-ai explainable-ml xai interpretable-machine-learning explainability multilabel-model xai-library

Updated Apr 6, 2022
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andrewli77 / DISC

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This repository contains an implementation of DISC, an algorithm for learning DFAs for multiclass sequence classification.

dfa sequence-classification interpretable-ai interpretable-ml dfa-construction early-detection interpretable-machine-learning automata-learning

Updated Feb 24, 2021
Python

PlantedML / Planted_Forest

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Interpretable machine learning algorithm

machine-learning random-forest ml decision-trees intelligibility interpretable-ml interpretable-machine-learning

Updated Feb 7, 2022
R

Jihunlee326 / InterpretableAI

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Interpretable AI with Safeguard AI (paper study, implement-code review)

interpretable-ai interpretable-ml

Updated Nov 11, 2018

parantapa / integrated-directional-gradients

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Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.

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

Updated Aug 25, 2021
Python

akifcinar / Machine_Learning_Interpretability

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Overview of machine learning interpretation techniques and their implementations

python data-science machine-learning transparency interpretation interpretability model-interpretation interpretable-ai interpretable-ml mli xai interpretable-machine-learning machine-learning-interpretability interpretable-classifcation

Updated Jan 5, 2021
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sumuzhao / Investigate-BERT-Non-linearity-Commutativity

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Investigate BERT on Non-linearity and Layer Commutativity

nlp transformers bert empirical-research interpretable-ml explainable-ml

Updated Apr 6, 2022
Python

marcovirgolin / CoGS

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A baseline genetic algorithm for the discovery of counterfactuals, implemented in Python for ease of use and heavily leveraging NumPy for speed.

genetic-algorithm interpretable-ai interpretable-ml explainable-ai explainable-ml interpretable-machine-learning counterfactual-explanations counterfactuals explainable-machine-learning

Updated Apr 11, 2022
Python

fpretto / interpretable_and_fair_ml

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Interpretability and Fairness in Machine Learning

interpretable-deep-learning interpretable-ai interpretable-ml interpretable-machine-learning fairness-ai model-interpretability fairness-ml

Updated Aug 5, 2020
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interpretable-ml

Here are 38 public repositories matching this topic...

interpretml / interpret

Interpret

pytorch / captum

jphall663 / awesome-machine-learning-interpretability

CSAILVision / gandissect

lopusz / awesome-interpretable-machine-learning

jphall663 / interpretable_machine_learning_with_python

h2oai / mli-resources

sergioburdisso / pyss3

12wang3 / rrl

chr5tphr / zennit

jphall663 / diabetes_use_case

jphall663 / hc_ml

navdeep-G / interpretable-ml

12wang3 / mllp

h2oai / article-information-2019

ben-ix / XAI

pnxenopoulos / cav-keras

slds-lmu / imlplots

schwettmann / visual-vocab

jphall663 / jsm_2018_paper

BirkhoffG / Explainable-ML-Papers

CeciPani / MARLENA

andrewli77 / DISC

PlantedML / Planted_Forest

Jihunlee326 / InterpretableAI

parantapa / integrated-directional-gradients

akifcinar / Machine_Learning_Interpretability

sumuzhao / Investigate-BERT-Non-linearity-Commutativity

marcovirgolin / CoGS

fpretto / interpretable_and_fair_ml

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