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27 search results found

Interpret ⭐ 5,992

Fit interpretable models. Explain blackbox machine learning.

Shapash ⭐ 2,547

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

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.

XAI - An eXplainability toolbox for machine learning

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]

Tf Keras Vis ⭐ 286

Neural network visualization toolkit for tf.keras

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

Graphxai ⭐ 128

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

Sagemaker Explaining Credit Decisions ⭐ 87

Amazon SageMaker Solution for explaining credit decisions.

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"

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.

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.

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.

Explanation Quality Recsys ⭐ 23

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

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

Xai Scholar ⭐ 16

Cross-field empirical trends analysis of XAI literature

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

A Toolbox for the Evaluation of machine learning Explanations

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

Awesome Time Series Explainability ⭐ 11

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

Pytolemaic ⭐ 10

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

Responsible Ai Workshop ⭐ 9

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

Counterfactual SHAP: a framework for counterfactual feature importance

Explainable Ml Papers ⭐ 5

A list of research papers of explainable machine learning.

1-27 of 27 search results

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