interpretability

A game theoretic approach to explain the output of any machine learning model.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

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

Fit interpretable models. Explain blackbox machine learning.

Model interpretability and understanding for PyTorch

A curated list of awesome responsible machine learning resources.

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

StellarGraph - Machine Learning on Graphs

Algorithms for explaining machine learning models

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM, Finer-CAM, LeGrad, RefineCAM)

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.

A collection of research materials on explainable AI/ML

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

Stanford NLP Python library for Representation Finetuning (ReFT)

moDel Agnostic Language for Exploration and eXplanation

XAI - An eXplainability toolbox for machine learning

open source interpretability platform 🧠

The nnsight package enables interpreting and manipulating the internals of deep learned models.

Interpretability Methods for tf.keras models with Tensorflow 2.x

Stanford NLP Python library for understanding and improving PyTorch models via interventions

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Locating and editing factual associations in GPT (NeurIPS 2022)

Shapley Interactions and Shapley Values for Machine Learning

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

👋 Xplique is a Neural Networks Explainability Toolbox

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

Code for the TCAV ML interpretability project