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109 search results found

Pytorch Grad Cam ⭐ 8,723

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

Interpret ⭐ 5,992

Fit interpretable models. Explain blackbox machine learning.

Tensorwatch ⭐ 3,333

Debugging, monitoring and visualization for Python Machine Learning and Data Science

Awesome Machine Learning Interpretability ⭐ 3,241

A curated list of awesome responsible machine learning resources.

Shapash ⭐ 2,547

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Awesome Explainable Graph Reasoning ⭐ 1,805

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

A library for graph deep learning research

Aix360 ⭐ 1,533

Interpretability and explainability of data and machine learning models

Generate Diverse Counterfactual Explanations for any machine learning model.

Dalex ⭐ 1,242

moDel Agnostic Language for Exploration and eXplanation

Imodels ⭐ 1,229

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Responsible Ai Toolbox ⭐ 1,187

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Trulens ⭐ 1,109

Evaluation and Tracking for LLM Experiments

XAI - An eXplainability toolbox for machine learning

Omnixai ⭐ 674

OmniXAI: A Library for eXplainable AI

Xplique ⭐ 522

👋 Xplique is a Neural Networks Explainability Toolbox

Aspect Based Sentiment Analysis ⭐ 413

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

Mli Resources ⭐ 405

H2O.ai Machine Learning Interpretability Resources

Explainx ⭐ 375

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]

Datascience_artificialintelligence_utils ⭐ 369

Examples of Data Science projects and Artificial Intelligence use-cases

Tf Keras Vis ⭐ 286

Neural network visualization toolkit for tf.keras

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Shapley ⭐ 198

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Explainable Cnn ⭐ 196

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

Explaining the output of machine learning models with more accurately estimated Shapley values

Graphxai ⭐ 128

GraphXAI: Resource to support the development and evaluation of GNN explainers

Xaience ⭐ 100

All about explainable AI, algorithmic fairness and more

Fastshap ⭐ 96

Fast approximate Shapley values in R

Sagemaker Explaining Credit Decisions ⭐ 87

Amazon SageMaker Solution for explaining credit decisions.

Fast and explainable clustering in Python

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

Seggradcam ⭐ 83

SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping

Tsinterpret ⭐ 83

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

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications

Explainable Machine Learning in Survival Analysis

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.

Fairmodels ⭐ 68

Flexible tool for bias detection, visualization, and mitigation

Ml Fairness Framework ⭐ 68

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Talktomodel ⭐ 68

TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!

Awesome Shapley Value ⭐ 63

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.

Xai Iml Sota ⭐ 50

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

Xplainable ⭐ 48

Real-time explainable machine learning for business optimisation

Prototree ⭐ 46

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

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

Clustershapley ⭐ 40

Explaining dimensionality results using SHAP values

Yolo Heatmaps ⭐ 32

A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).

Global Attribution Mapping ⭐ 31

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

Model Agnostic Counterfactual Explanations

Shap_fold ⭐ 28

(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining

Ilasp Releases ⭐ 26

Sirus.jl ⭐ 25

Interpretable Machine Learning via Rule Extraction

Strategic Decisions ⭐ 24

Code and data for decision making under strategic behavior

Xai_thyroid ⭐ 24

All Classification and Object Detection XAI methods (CAM-based, backpropagation-based, perturbation-based, statistic-based) for thyroid cancer ultrasound images

Hyperbox Brain ⭐ 23

A scikit-learn compatible hyperbox-based machine learning library in Python

Explanation Quality Recsys ⭐ 23

Post Processing Explanations Paths in Path Reasoning Recommender Systems with Knowledge Graphs

Diabetes_use_case ⭐ 19

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

Cnn Raccoon ⭐ 19

Create interactive dashboards for your Convolutional Neural Networks with a single line of code!

a tool for comparing the predictions of any text classifiers

A Python package with explanation methods for extraction of feature interactions from predictive models

Pytorch Lattice ⭐ 18

A PyTorch implementation of constrained optimization and modeling techniques

Xai Scholar ⭐ 16

Cross-field empirical trends analysis of XAI literature

Dlime_experiments ⭐ 16

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Genetic programming method for explaining complex black-box models

A python package for semi-structured deep distributional regression

Health Fact Checking ⭐ 15

Dataset and code for "Explainable Automated Fact-Checking for Public Health Claims" from EMNLP 2020.

Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.

Explainableml Vision ⭐ 14

This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.

Identifiable Transformers ⭐ 14

Thesis Designing Recurrent Neural Networks For Explainability ⭐ 14

A Toolbox for the Evaluation of machine learning Explanations

Rough set and machine learning data structures, algorithms and tools, including algorithms for discernibility matrix, reducts, decision rules, classification (RoughSet, KNN, RIONIDA, AQ15, C4.5, SVM, NeuralNetwork and many others), discretization (1R, Entropy Minimization, ChiMerge, MD), and tool for interactive and explainable machine learning.

Variable importance via oscillations

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

Recourse ⭐ 12

Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831

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

Awesome Time Series Explainability ⭐ 11

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

Article Information 2019 ⭐ 10

Article for Special Edition of Information: Machine Learning with Python

Pytolemaic ⭐ 10

Toolbox for analysis of model's quality and model's description. For further details see

Counterfactual Tpp ⭐ 10

Code and real data for the paper "Counterfactual Temporal Point Processes", NeurIPS 2022

Malnet Image ⭐ 9

A large-scale database of malicious software images

Responsible Ai Workshop ⭐ 9

Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Learning Scaffold ⭐ 9

This is the official implementation for the paper "Learning to Scaffold: Optimizing Model Explanations for Teaching"

DashAI provides a simple graphical user interface (GUI) that guides users through a step-by-step process through creating, training, and saving a model.

Transparency Guidelines ⭐ 8

Minimize the risks and maximize the benefits of using data-driven technologies within government processes, programs and services through transparency. | Réduire les risques et à maximiser les avantages liés à l’utilisation de technologies axées sur les données, dans le cadre de processus, programmes et services gouvernementaux, grâce à la transparence.

Iai Clinical Decision Rule ⭐ 8

Interpretable clinical decision rules for predicting intra-abdominal injury.

Automated Transparent Genetic Feature Engineering

Jsm_2018_paper ⭐ 8

Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlinepro

Visual Explanation using Uncertainty based Class Activation Maps

Explore/examine/explain/expose your model with the explabox!

Implementation of "Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps"

A collection of algorithms of counterfactual explanations.

A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.

Xai Analytics ⭐ 7

XAI-Analytics is a tool that opens the black-box of machine learning. It helps the user to understand the decision-making process of machine learning models.

Counterfactual Explanations Mdp ⭐ 7

Code for "Counterfactual Explanations in Sequential Decision Making Under Uncertainty", NeurIPS 2021

A New Method for the Interpretability of Composite Decision Systems.

Interpretablesdmwithjulia ⭐ 6

Slides for the "Interpretable SDM with Julia" workshop

Aix360 Introduction ⭐ 6

Introduction to explaining data and machine learning models with aif360

Deep learning for personalized interpretability for compositional health data

1-100 of 109 search results

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