GitHub topics: explainable-ai

Repositories

albertovalerio/brain-tumor-segmentation-with-explainability

The primary objective of this work is to develop an innovative system capable of providing explainable brain tumor detection.

Language: Jupyter Notebook - Size: 150 MB - Last synced at: 25 days ago - Pushed at: 26 days ago - Stars: 1 - Forks: 0

Aronno1920/AI-Engineering

AI Engineering Bootcamp for Programmers - A 19-module, 18-week immersive study plan designed for programmers looking to master AI engineering through real-world projects, foundational theory, and practical tools.

Language: Python - Size: 24.4 KB - Last synced at: 26 days ago - Pushed at: 26 days ago - Stars: 0 - Forks: 0

dlcjfgmlnasa/NeuroXAI

[ESWA] NeuroXAI: Adaptive, Robust, Explainable Surrogate Framework for Determination of Channel Importance in EEG Application

Language: Python - Size: 862 KB - Last synced at: 19 days ago - Pushed at: 6 months ago - Stars: 23 - Forks: 2

s-marton/SYMPOL

(ICLR 2025) Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization

Language: Python - Size: 746 KB - Last synced at: 21 days ago - Pushed at: 10 months ago - Stars: 19 - Forks: 1

ModelOriented/survex

Explainable Machine Learning in Survival Analysis

Language: R - Size: 309 MB - Last synced at: 21 days ago - Pushed at: about 1 year ago - Stars: 112 - Forks: 10

ejml1/Deep-Learning-for-Cancer-Detection

An explainable Convolutional Neural Network to classify single cells from bone marrow smears as part of a BSc Dissertation at the University of St Andrews

Language: Jupyter Notebook - Size: 556 MB - Last synced at: 26 days ago - Pushed at: 26 days ago - Stars: 1 - Forks: 0

explainX/explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]

Language: Jupyter Notebook - Size: 61.3 MB - Last synced at: 25 days ago - Pushed at: 10 months ago - Stars: 436 - Forks: 56

ModelOriented/shapviz

SHAP Plots in R

Language: R - Size: 41.2 MB - Last synced at: 6 days ago - Pushed at: 2 months ago - Stars: 98 - Forks: 15

tameTNT/evaluating-xai-remote-sensing

My final year (level 3) self-proposed dissertation project undertaken at Durham University in 2024/25 covering satellite imagery, deep learning, explainable AI (xAI), and evaluation metrics for xAI. Submitted to ECAI2025.

Language: Jupyter Notebook - Size: 170 MB - Last synced at: 26 days ago - Pushed at: 26 days ago - Stars: 0 - Forks: 0

ModelOriented/treeshap

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Language: R - Size: 19.7 MB - Last synced at: 15 days ago - Pushed at: 11 months ago - Stars: 88 - Forks: 24

AndMastro/EdgeSHAPer

EdgeSHAPer: Bond-Centric Shapley Value-Based Explanation Method for Graph Neural Networks

Language: Jupyter Notebook - Size: 2.13 GB - Last synced at: 27 days ago - Pushed at: 27 days ago - Stars: 26 - Forks: 1

rachtibat/zennit-crp

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization

Language: Jupyter Notebook - Size: 17.9 MB - Last synced at: 27 days ago - Pushed at: about 1 year ago - Stars: 127 - Forks: 18

jacobgil/vit-explain

Explainability for Vision Transformers

Language: Python - Size: 4.11 MB - Last synced at: 25 days ago - Pushed at: over 3 years ago - Stars: 958 - Forks: 103

ScalaConsultants/Aspect-Based-Sentiment-Analysis

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

Language: Python - Size: 1.8 MB - Last synced at: 20 days ago - Pushed at: about 2 months ago - Stars: 570 - Forks: 90

deel-ai/xplique

👋 Xplique is a Neural Networks Explainability Toolbox

Language: Python - Size: 33.4 MB - Last synced at: 1 day ago - Pushed at: 8 months ago - Stars: 689 - Forks: 58

fzi-forschungszentrum-informatik/TSInterpret

An Open-Source Library for the interpretability of time series classifiers

Language: Python - Size: 200 MB - Last synced at: 2 days ago - Pushed at: 7 months ago - Stars: 134 - Forks: 15

adaamko/POTATO

XAI based human-in-the-loop framework for automatic rule-learning.

Language: Jupyter Notebook - Size: 6.07 MB - Last synced at: 14 days ago - Pushed at: 12 months ago - Stars: 49 - Forks: 8

kevinqnb/interpretable-clustering

Methods and algorithms for interpretable clustering. Example applications to climate data.

Language: Python - Size: 208 MB - Last synced at: 13 days ago - Pushed at: 28 days ago - Stars: 0 - Forks: 0

EthicalML/xai

XAI - An eXplainability toolbox for machine learning

Language: Python - Size: 17.8 MB - Last synced at: 26 days ago - Pushed at: over 3 years ago - Stars: 1,171 - Forks: 179

HumbertoSGoncalves/SeeThroughPackets

Enhanced PacketWorx with Explainable AI (SHAP & LIME) for interpretable intrusion detection. Uses CIC-IDS2017-based ML models to classify and explain packet-level threats. Built for cybersecurity analysts and product managers seeking transparency in detection logic.

Language: Python - Size: 1.11 MB - Last synced at: 29 days ago - Pushed at: 29 days ago - Stars: 0 - Forks: 0

Devinterview-io/explainable-ai-interview-questions

🟣 Explainable Ai interview questions and answers to help you prepare for your next machine learning and data science interview in 2025.

Size: 32.2 KB - Last synced at: 29 days ago - Pushed at: 29 days ago - Stars: 7 - Forks: 4

ottenbreit-data-science/aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

Language: C++ - Size: 6.13 MB - Last synced at: 25 days ago - Pushed at: 2 months ago - Stars: 19 - Forks: 5

PeeteKeesel/coursera-summaries

:mag: Labs, quizzes, answers and explanations for Coursera specialisations and courses

Language: Jupyter Notebook - Size: 91 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 2 - Forks: 0

Samia35-2973/Deep-Learning-for-Explainable-Traffic-Anomaly-Detection-in-Dhaka

The project introduces a Multi-Stage Traffic Anomaly Analysis Framework for identifying and analyzing urban traffic congestion, particularly in Dhaka. Originally utilizing Faster R-CNN and DBSCAN, it has been upgraded to state-of-the-art YOLOv9e and YOLOv10l models for enhanced accuracy and efficiency.

Language: Jupyter Notebook - Size: 28.5 MB - Last synced at: 1 day ago - Pushed at: 4 months ago - Stars: 2 - Forks: 0

Wondermongering/Cultural-Neural-Hermeneutics

Interdisciplinary research project exploring AI bias, interpretability, and cultural influence through computational models trained on diverse philosophical corpora. Python, PyTorch, Transformers, UMAP, Streamlit.

Size: 20.5 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

jacobgil/pytorch-grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Language: Python - Size: 134 MB - Last synced at: 30 days ago - Pushed at: 2 months ago - Stars: 11,641 - Forks: 1,636

x66ccff/SymbolicRegressionGPU.jl Fork of MilesCranmer/SymbolicRegression.jl

PSRN (Parallel Symbolic Regression Network) enhanced SymbolicRegression.jl via fast, large-scale parallel symbolic evaluations on GPUs

Language: Julia - Size: 19.1 MB - Last synced at: 22 days ago - Pushed at: 22 days ago - Stars: 7 - Forks: 1

Eduardo-J-Morales/NeuroFlow-AI

This web app uses an AI model trained with TensorFlow.js to guess if a (fake) person would be "focused" or "distracted" based on simulated brain waves (alpha/beta/gamma) generated every second.

Language: JavaScript - Size: 89.8 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

DmitryRyumin/ICCV-2023-Papers

ICCV 2023 Papers: Discover cutting-edge research from ICCV 2023, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!

Language: Python - Size: 16.8 MB - Last synced at: 26 days ago - Pushed at: 10 months ago - Stars: 953 - Forks: 43

UMCUtrecht-ECGxAI/ecgxai

Neatly packaged AI methods for explainable ECG analysis

Language: Python - Size: 611 KB - Last synced at: 25 days ago - Pushed at: over 1 year ago - Stars: 71 - Forks: 17

MI2DataLab/survshap

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Language: Jupyter Notebook - Size: 8.99 MB - Last synced at: 21 days ago - Pushed at: over 1 year ago - Stars: 87 - Forks: 16

agrumery/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).

Language: Jupyter Notebook - Size: 335 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 40 - Forks: 0

vijaysr4/MMEL

Research Project 1 - Multimodal Entity Linking with VLMs on WikiData

Language: Python - Size: 39.1 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

burhanahmed1/CryptoSynth

Bitcoin Sentiment Forecast is a Multimodal approach to Bitcoin price forecasting using NLP and Time Series Analysis

Language: Jupyter Notebook - Size: 3.54 MB - Last synced at: 3 days ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

Ki-Seki/Awesome-Transformer-Visualization

Explore visualization tools for understanding Transformer-based large language models (LLMs)

Size: 23.4 MB - Last synced at: 3 days ago - Pushed at: 7 months ago - Stars: 12 - Forks: 2

jpmorganchase/cf-shap

Counterfactual SHAP: a framework for counterfactual feature importance

Language: HTML - Size: 823 KB - Last synced at: 6 days ago - Pushed at: almost 2 years ago - Stars: 20 - Forks: 9

AstraZeneca/awesome-explainable-graph-reasoning

A collection of research papers and software related to explainability in graph machine learning.

Size: 1.7 MB - Last synced at: 6 days ago - Pushed at: about 3 years ago - Stars: 1,963 - Forks: 130

Sanofi-Public/Clinical-BERT-Explainability

To explain clinical BERT model predictions, we present an approach which leverages integrated gradients to attribute events in medical records that lead to an outcome prediction.

Language: Python - Size: 768 KB - Last synced at: 5 days ago - Pushed at: 5 months ago - Stars: 7 - Forks: 0

WilliamCCHuang/GraphLIME

This is a Pytorch implementation of GraphLIME

Language: Jupyter Notebook - Size: 646 KB - Last synced at: about 1 month ago - Pushed at: over 3 years ago - Stars: 91 - Forks: 14

HuwCheston/deep-pianist-identification

Code from: Deconstructing Jazz Piano Style Using Machine Learning

Language: Python - Size: 8.92 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 1 - Forks: 0

ModelOriented/DALEX

moDel Agnostic Language for Exploration and eXplanation

Language: Python - Size: 798 MB - Last synced at: 28 days ago - Pushed at: 4 months ago - Stars: 1,422 - Forks: 168

aloth/JudgeGPT

JudgeGPT - (Fake) News Evaluation, a research project

Language: Python - Size: 640 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 4 - Forks: 1

hila-chefer/Transformer-MM-Explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Language: Jupyter Notebook - Size: 25.3 MB - Last synced at: 25 days ago - Pushed at: almost 2 years ago - Stars: 851 - Forks: 110

chr5tphr/zennit

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

Language: Python - Size: 2.28 MB - Last synced at: 24 days ago - Pushed at: 11 months ago - Stars: 226 - Forks: 34

jasjeev013/GeoMineralInsight

This project uses machine learning to analyze geological, geochemical, aeromagnetic, and remote sensing data over 39,000 sq. km in southern India. It identifies high-probability zones for concealed Au, Cu, and PGE deposits using XGBoost, SHAP, and GeoPandas. Key features include automated pipelines, explainable AI, and GIS-ready maps.

Language: Jupyter Notebook - Size: 7.38 MB - Last synced at: 14 days ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

intel/intel-xai-tools

Explainable AI Tooling (XAI). XAI is used to discover and explain a model's prediction in a way that is interpretable to the user. Relevant information in the dataset, feature-set, and model's algorithms are exposed.

Language: Jupyter Notebook - Size: 118 MB - Last synced at: 11 days ago - Pushed at: about 1 month ago - Stars: 39 - Forks: 7

BCG-X-Official/facet

Human-explainable AI.

Language: Jupyter Notebook - Size: 50.5 MB - Last synced at: 26 days ago - Pushed at: over 1 year ago - Stars: 521 - Forks: 47

IDT-ITI/P-TAME

Scripts and trained models from our paper: M. Ntrougkas, V. Mezaris, I. Patras, "P-TAME: Explain Any Image Classifier with Trained Perturbations", IEEE Open Journal of Signal Processing, 2025. DOI:10.1109/OJSP.2025.3568756.

Language: Python - Size: 152 KB - Last synced at: 5 days ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

JHoelli/Awesome-Time-Series-Explainability

A list of (post-hoc) XAI for time series

Size: 424 KB - Last synced at: about 1 month ago - Pushed at: 10 months ago - Stars: 135 - Forks: 16

AapseMatlb/pickasso-hmi

A real-time Human-Machine Interface (HMI) Dashboard for the Pickasso autonomous trash-collecting robot. Features include voice command control, real-time robot status monitoring, explainability of robot decisions, and manual override capabilities. Built using React, Convex Cloud, and Tailwind CSS for responsive and modern UI.

Language: TypeScript - Size: 242 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

timm/barelogic

Case studies in simpler AI, elegance, and the wisdom of reduction. Think Fast. Learn Smart. Stay Wild.

Language: Python - Size: 7.25 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 3 - Forks: 10

rikardvinge/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.

Language: Python - Size: 30.4 MB - Last synced at: 6 days ago - Pushed at: about 1 month ago - Stars: 2 - Forks: 0

InflixOP/Anomaly-detection-using-Explainable-AI

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

Language: Jupyter Notebook - Size: 91.5 MB - Last synced at: about 1 month ago - Pushed at: 2 months ago - Stars: 7 - Forks: 0

aerdem4/lofo-importance

Leave One Feature Out Importance

Language: Python - Size: 782 KB - Last synced at: 26 days ago - Pushed at: 4 months ago - Stars: 835 - Forks: 86

natnew/Awesome-Data-Science

Carefully curated list of awesome data science resources.

Size: 3.44 MB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 137 - Forks: 22

ChandanaThummala/Explainable-AI-for-Breast-Cancer-Prediction

Breast Cancer Classification with Explainable AI (SHAP, LIME, PDP)

Language: Jupyter Notebook - Size: 2.86 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

DecioXXIV/xAI-WriterIdentification

Explainable Artificial Intelligence applied to the Handwriting Identification task, carried out with CNNs

Language: Python - Size: 75.1 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 1

ritu-thombre99/explaining_quanvolution

This work explores whether the quanvolution neural network is explainable by proposing a novel mathematical approach for quantifying explainability

Language: Jupyter Notebook - Size: 840 MB - Last synced at: 14 days ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

XAITK/xaitk-saliency

As part of the Explainable AI Toolkit (XAITK), XAITK-Saliency is an open source, explainable AI framework for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications.

Language: Python - Size: 23.9 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 91 - Forks: 25

AbidHasanRafi/histopathology-cancer-classifier-app

This application is a deep learning-based system for classifying histopathology images of lung and colon tissues into five distinct categories

Language: Python - Size: 3.45 MB - Last synced at: 17 days ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 1

wangyongjie-ntu/Awesome-explainable-AI

A collection of research materials on explainable AI/ML

Language: Markdown - Size: 1.93 MB - Last synced at: about 1 month ago - Pushed at: 3 months ago - Stars: 1,494 - Forks: 203

Smit-Parekh/deep-demand-forecast-retail

End-to-end Deep Learning (TFT) demand forecasting system for Retail/FMCG with automated MLOps pipeline on Google Cloud (Vertex AI) for inventory optimization. Demonstrates advanced time series modeling, feature engineering, explainability (SHAP), and scalable deployment.

Size: 3.91 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

pietrobarbiero/pytorch_explain

PyTorch Explain: Interpretable Deep Learning in Python.

Language: Jupyter Notebook - Size: 42.1 MB - Last synced at: 30 days ago - Pushed at: about 1 year ago - Stars: 154 - Forks: 14

pat-alt/endogenous-macrodynamics-in-algorithmic-recourse

Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)

Language: HTML - Size: 143 MB - Last synced at: 3 days ago - Pushed at: 22 days ago - Stars: 1 - Forks: 0

dynamical-inference/patchsae

Implementation of PatchSAE as presented in "Sparse autoencoders reveal selective remapping of visual concepts during adaptation"

Language: Jupyter Notebook - Size: 14.8 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 12 - Forks: 0

YouhuiPang/FX-Risk-Forecasting-System

An intuitive end-to-end web-app system that forecasts FX risk over the next 3 days, powered by explainable AI and real market data.

Language: Python - Size: 3.93 MB - Last synced at: 14 days ago - Pushed at: about 1 month ago - Stars: 2 - Forks: 0

UniBwTAS/CollisionPro

Towards explainable value functions in reinforcement learning. A framework for collision probability distribution estimation via deep temporal difference learning.

Language: Python - Size: 2.85 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 12 - Forks: 2

LamineTourelab/Explainable-AI

In this repository you will fine explainability of machine learning models.

Size: 8.79 KB - Last synced at: 16 days ago - Pushed at: over 2 years ago - Stars: 9 - Forks: 0

bgreenwell/ebm

Explainable Boosting Machines

Language: R - Size: 44.5 MB - Last synced at: 9 days ago - Pushed at: 3 months ago - Stars: 3 - Forks: 1

keisen/tf-keras-vis

Neural network visualization toolkit for tf.keras

Language: Python - Size: 97.1 MB - Last synced at: 20 days ago - Pushed at: 3 months ago - Stars: 325 - Forks: 45

yash-srivastava19/arrakis

Arrakis is a library to conduct, track and visualize mechanistic interpretability experiments.

Language: Jupyter Notebook - Size: 3.53 MB - Last synced at: 20 days ago - Pushed at: about 2 months ago - Stars: 29 - Forks: 2

ArashAkbarinia/osculari

Exploring and interpreting pretrained deep neural networks.

Language: Python - Size: 2.23 MB - Last synced at: 29 days ago - Pushed at: 11 months ago - Stars: 6 - Forks: 0

holistic-ai/holisticai

This is an open-source tool to assess and improve the trustworthiness of AI systems.

Language: Jupyter Notebook - Size: 90.4 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 90 - Forks: 24

GuanRunwei/Awesome-Vision-Transformer-Collection

Variants of Vision Transformer and its downstream tasks

Size: 59.6 KB - Last synced at: about 1 month ago - Pushed at: almost 3 years ago - Stars: 234 - Forks: 28

lopusz/awesome-interpretable-machine-learning

Language: Python - Size: 1.47 MB - Last synced at: about 1 month ago - Pushed at: about 2 years ago - Stars: 916 - Forks: 139

PERSIMUNE/explainer

ExplaineR is an R package built for enhanced interpretation of classification and regression models based on SHAP method and interactive visualizations with unique functionalities so please feel free to check it out, See ExplaineR paper at doi:10.1093/bioadv/vbae049

Language: R - Size: 17.8 MB - Last synced at: 26 days ago - Pushed at: 9 months ago - Stars: 17 - Forks: 1