XAI - An eXplainability toolbox for machine learning

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Shapley Interactions and Shapley Values for Machine Learning

Official implementation of Score-CAM in PyTorch

This is an open-source version of the representation engineering framework for stopping harmful outputs or hallucinations on the level of activations. 100% free, self-hosted and open-source.

Neural network visualization toolkit for tf.keras

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

For calculating global feature importance using Shapley values.

Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch

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

Can we use explanations to improve hate speech models? Our paper accepted at AAAI 2021 tries to explore that question.

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

[Not Actively Maintained] Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes

🗺️ Data Cleaning and Textual Data Visualization 🗺️

Interpret text data using LLMs (scikit-learn compatible).

Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness

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

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

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"

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