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

Fit interpretable models. Explain blackbox machine learning.

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

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

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

🪄 Interactive Diagrams for Code

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Power Tools for AI Engineers With Deadlines

Papers about explainability of GNNs

Visualization toolkit for neural networks in PyTorch! Demo -->

Shapley Interactions and Shapley Values for Machine Learning

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

[Pattern Recognition 25] CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks

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 @ ms8909@nyu.edu

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

💡 Adversarial attacks on explanations and how to defend them

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

Visualization tool for Graph Neural Networks

OpenXAI : Towards a Transparent Evaluation of Model Explanations

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

🗺️ Data Cleaning and Textual Data Visualization 🗺️

Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision.