# Topics > Related topics > Explainable AI (XAI) >  InterpretML, open-source software toolkit for explaining black box AI, Microsoft Corporation, Redmond, Washington, USA

## Airicist

Developer - Microsoft Corporation

interpret.ml
interpretm.ai

github.com/interpretml

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## Airicist

Article "Microsoft open-sources lnterpretML for explaining black box AI"

by Khari Johnson
May 10, 2019

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## Airicist

Article "InterpretML: A Unified Framework for Machine Learning Interpretability"

by Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana
September 19, 2019

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## Airicist

"Explain Your Model with Microsoft’s InterpretML"

Feb 27, 2020

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## Airicist

How to explain models with IntepretML Deep Dive

May 16, 2020




> With the recent popularity of machine learning algorithms such as neural networks and ensemble methods, etc., machine learning models become more like a 'black box', harder to understand and interpret. To gain the stakeholders' trust, there is a strong need to develop tools and methodologies to help the user to understand and explain how predictions are made. In this video, you learn about our open source Machine Learning Interpretability toolkit, InterpretML, which incorporates the cutting-edge technologies developed by Microsoft and leverages proven third-party libraries. InterpretML introduces a state-of-the-art glass box model (EBM), and provides an easy access to a variety of other glass box models and blackbox explainers.

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## Airicist

The science behind InterpretML: explainable boosting machine

May 16, 2020




> Learn more about the research that powers InterpretML from Explainable Boosting Machine creator, Rich Caurana from Microsoft Research

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