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27 search results found

Interpret ⭐ 5,992

Fit interpretable models. Explain blackbox machine learning.

Tensorwatch ⭐ 3,333

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

Giskard ⭐ 2,509

🐢 The testing framework for ML models, from tabular to LLMs

Aix360 ⭐ 1,533

Interpretability and explainability of data and machine learning models

Imodels ⭐ 1,229

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

XAI - An eXplainability toolbox for machine learning

Machine_learning_tutorials ⭐ 421

Code, exercises and tutorials of my personal blog ! 📝

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

Modelstudio ⭐ 305

📍 Interactive Studio for Explanatory Model Analysis

Adversarial Explainable Ai ⭐ 235

💡 Adversarial attacks on explanations and how to defend them

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

Hierarchical Dnn Interpretations ⭐ 119

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Xaience ⭐ 100

All about explainable AI, algorithmic fairness and more

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

Deep Explanation Penalization ⭐ 74

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Essential NLP & ML, short & fast pure Python code

Awesome Shapley Value ⭐ 63

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

Fast Tsetlin Machine With Mnist Demo ⭐ 52

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

Neuro Symbolic Sudoku Solver ⭐ 44

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.

WikiWhy is a new benchmark for evaluating LLMs' ability to explain between cause-effect relationships. It is a QA dataset containing 9000+ "why" question-answer-rationale triplets.

Finding semantically meaningful and accurate prompts.

Familiar ⭐ 27

Repository for the familiar R-package. Familiar implements an end-to-end pipeline for interpretable machine learning of tabular data.

Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off"

Intel Xai Tools ⭐ 23

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.

Disentangled Attribution Curves ⭐ 23

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

This package implements the trustee framework to extract decision tree explanation from black-box ML models.

Xai Demonstrator ⭐ 17

The XAI Demonstrator is a modular platform that lets users interact with production-grade Explainable AI (XAI) systems.

Javaanchorexplainer ⭐ 17

Explains machine learning models fast using the Anchor algorithm originally proposed by marcotcr in 2018

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).

This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.

Awesome Data Science ⭐ 15

Carefully curated list of awesome data science resources.

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

Cnn_explainer ⭐ 11

Making CNNs interpretable.

Supporting code for doi 10.1021/acs.jcim.0c01344

Label Free Xai ⭐ 10

This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.

Pytolemaic ⭐ 10

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

Analyzing and scoring reasoning traces of LLMs

Iai Clinical Decision Rule ⭐ 8

Interpretable clinical decision rules for predicting intra-abdominal injury.

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.

Local Individual Conditional Expectation (localICE) is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI)

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

Explaining Deep Clinical Gait Classification ⭐ 6

Code and Data used for the paper "Explaining Machine Learning Models for Clinical Gait Analysis"

XAI & AI college Recording Repo

Deep Learning Resources ⭐ 5

Curated list of DL Resources [Updated 2019]

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