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

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

Article for Special Edition of Information: Machine Learning with Python

H2O.ai Machine Learning Interpretability Resources

Overview of machine learning interpretation techniques and their implementations

Fairness in AI and Machine Learning

Default Risk Prediction from bank dataset with Interpretable Machine Learning

XMLX GitHub configuration

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

Rule Extraction from Bayesian Networks

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

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

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".

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.

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

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

Demonstration of InterpretME, an interpretable machine learning pipeline

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

An interpretable machine learning pipeline over knowledge graphs