Google Chromium, sans integration with Google

Fit interpretable models. Explain blackbox machine learning.

A curated list of awesome responsible machine learning resources.

Promoting Solutions that Improve Wellbeing, Freedom and Society

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

We're tracking all our Issues, RFCs and a few other documents in this repository.

Ever® Gauzy™ - Open Business Management Platform (ERP/CRM/HRM)

SmartSystemMenu extends system menu of all windows in the system

A comparison between some VPS providers. It uses Ansible to perform a series of automated benchmark tests over the VPS servers that you specify. It allows the reproducibility of those tests by anyone that wanted to compare these results to their own. All the tests results are available in order to provide independence and transparency.

My public self and book of beliefs.

Tor2web is an HTTP proxy software that enables access to Tor Hidden Services by mean of common web browsers

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

Educational material to learn about Goggles and how to create your own.

H2O.ai Machine Learning Interpretability Resources

A toolkit that streamlines and automates the generation of model cards

Data from the largest and longest measurement of online tracking.

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

Unity material and shader for applying clipped, dithered transparency

Mapeamento de iniciativas (e catálogos) de dados abertos governamentais no Brasil.

💎Tops of VSCode Extensions