A curated list of awesome responsible machine learning resources.

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

H2O.ai Machine Learning Interpretability Resources

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

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

An interpretable machine learning pipeline over knowledge graphs

Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Article for Special Edition of Information: Machine Learning with Python

Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539

TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning

Rule Extraction from Bayesian Networks

INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.

Overview of machine learning interpretation techniques and their implementations

Demonstration of InterpretME, an interpretable machine learning pipeline

This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".

XMLX GitHub configuration

XMLX GitHub configuration