imodels
imodels: a python package for fitting interpretable models - Published in JOSS (2021)
modelStudio
modelStudio: Interactive Studio with Explanations for ML Predictive Models - Published in JOSS (2019)
shapr
shapr: An R-package for explaining machine learning models with dependence-aware Shapley values - Published in JOSS (2019)
SIRUS.jl
SIRUS.jl: Interpretable Machine Learning via Rule Extraction - Published in JOSS (2023)
FAT Forensics
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems - Published in JOSS (2020)
pyCeterisParibus
pyCeterisParibus: explaining Machine Learning models with Ceteris Paribus Profiles in Python - Published in JOSS (2019)
sahi
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
TSInterpret
TSInterpret: A Python Package for the Interpretability of Time Series Classification - Published in JOSS (2023)
quantus
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
CounterfactualExplanations
A package for Counterfactual Explanations and Algorithmic Recourse in Julia.
sai
Using explainable to identify regional climate signals to stratospheric aerosol injection
transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
privacy-meter
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
mlm-bias
Measuring Biases in Masked Language Models for PyTorch Transformers. Support for multiple social biases and evaluation measures.
grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
GSAreport
GSAreport: Easy to Use Global Sensitivity Reporting - Published in JOSS (2022)
detectmitigate
Using neural networks to detect effects of rapid climate mitigation
predictgmstrate
Using a neural network to predict changes in the rate of global mean surface temperature warming
modelbiasesann
Investigation of model biases in historical internal variability using explainable AI
phepy
Intuitive evaluation of out-of-distribution detectors using simple toy examples.
povertymaps
Interpreting wealth distribution via poverty map inference using multimodal data
gam
GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations
hierarchical-dnn-interpretations
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
slise
Robust regression algorithm that can be used for explaining black box models (Python implementation)
https://github.com/csinva/disentangled-attribution-curves
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
https://github.com/ammarlodhi255/fine-grained-approach-to-wrist-pathology-recognition
This repository contains the official code for the paper "Learning from the Few: Fine-grained Approach to Wrist Pathology Recognition on a Limited Dataset".
https://github.com/andreartelt/ceml
CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox
cpath
Explaining black-box models through counterfactual paths and conditional permutations
neurox
A Python library that encapsulates various methods for neuron interpretation and analysis in Deep NLP models.
https://github.com/agamiko/gebi
GEBI: Global Explanations for Bias Identification. Open source code for discovering bias in data with skin lesion dataset
azimuth
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
https://github.com/csinva/iprompt
Finding semantically meaningful and accurate prompts.
crm
Compositional Relational Machines (CRMs): Constructing deep neural networks that are logically explainable by design
https://github.com/birkhoffg/rocoursenet
This is the official repository of the paper "RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model".
tabsplanation
Experiments on counterfactual explanations for neural networks, based on the [latent shift method](https://arxiv.org/abs/2102.09475)
slisemap
SLISEMAP: Combining supervised dimensionality reduction with local explanations
https://github.com/csinva/clinical-rule-development
Building and vetting clinical decision rules.
araucana-xai
Tree-based local explanations of machine learning model predictions
explainable-crack-tip-detection
Explainable ML for fatigue crack tip detection - Implementation
additive-sparse-boost-regression
A Python Package for a Sparse Additive Boosting Regressor
https://github.com/arashakbarinia/deepths
A framework to compute threshold sensitivity of deep networks to visual stimuli.
https://github.com/csinva/transformation-importance
Using / reproducing TRIM from the paper "Transformation Importance with Applications to Cosmology" 🌌 (ICLR Workshop 2020)
https://github.com/astrazeneca/awesome-shapley-value
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
https://github.com/cair/fast-tsetlin-machine-with-mnist-demo
A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.
https://github.com/holistic-ai/holisticai
This is an open-source tool to assess and improve the trustworthiness of AI systems.
https://github.com/pietrobarbiero/logic_explained_networks
Logic Explained Networks is a python repository implementing explainable-by-design deep learning models.
finer-cam
This is an official implementation for Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation. [CVPR'25]
verbal-explanations-of-spatio-temporal-graph-neural-networks-for-traffic-forecasting
An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.
slise
Robust regression algorithm that can be used for explaining black box models (R implementation)
modeling-uncertainty-local-explainability
Local explanations with uncertainty 💐!
explainable-cell-graphs
Code and experiments of the Explainable Cell Graphs (xCG) paper
arrakis-mi
Arrakis is a library to conduct, track and visualize mechanistic interpretability experiments.
3d-viz-score-cam
Visualizing 3D ResNet for Medical Image Classification With Score-CAM
survex
Explainable Machine Learning in Survival Analysis
localice
Local Individual Conditional Expectation (localICE) is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI)
explainpolysvm
ExplainPolySVM is a python package to provide interpretation and explainability to Support Vector Machine models trained with polynomial kernels. The package can be used with any SVM model as long as the components of the model can be extracted.
agrum
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
contrastiveexplanation
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University
intr
This is an official implementation for [ICLR'24] INTR: Interpretable Transformer for Fine-grained Image Classification.