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

Power Tools for AI Engineers With Deadlines

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

[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.

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

Papers about explainability of GNNs

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

Neural network visualization toolkit for tf.keras

CLIP Surgery for Better Explainability with Enhancement in Open-Vocabulary Tasks

💡 Adversarial attacks on explanations and how to defend them

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

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

For calculating global feature importance using Shapley values.