MARLeME

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.

Java 1-21 Parser and Abstract Syntax Tree for Java with advanced analysis functionalities.

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

Algorithms for explaining 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.

moDel Agnostic Language for Exploration and eXplanation

Model explainability that works seamlessly with ? transformers. Explain your transformers model in just 2 lines of code.