Using the ‘What-If Tool’ to investigate Machine Learning models

This blog post by Parul Pandey has a nice description of the resources available around the Google "What-if Tool". 

What-If Tool is an interactive visual tool that is designed to investigate the Machine Learning models. Abbreviated as WIT, it enables the understanding of a Classification or Regression model by enabling people to examine, evaluate, and compare machine learning models. Due to its user-friendly interface and less dependency on complex coding, everyone from a developer, a product manager, a researcher or a student can use it for their purpose.

Be sure to see the essay titled, "Playing with AI Fairness" by David Weinberger which talks about using the What-if tool environment to "try on five different types of fairness". I found it to be an accessible introduction to the different approaches to making real world decisions with ML tools. 

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Sam S Donovan onto Machine Learning

Teach Data Science Blog

A roadmap to recent developments in teaching data science

In this blog, we’re hoping to create a roadmap for faculty development that will ease the learning curve and help busy people incorporate new tools and approaches into their teaching.

Each day during the summer we intend to add a new entry on a given topic, along with a short overview of why it is interesting and how it can be applied to teaching. We intend to make the entries short, succinct, and easy to comprehend with the goal that they will motivate you to dive deeper. 

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There is also an associated Google Group where some discussions are occurring

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Datamethods Discussion Forum

This is a place where statisticians, epidemiologists, informaticists, machine learning practitioners, and other research methodologists communicate with themselves and with clinical, translational, and health services researchers to discuss issues related to data...

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Created by Frank Harrell

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Road Map for Choosing Between Statistical Modeling and Machine Learning

from the Statistical Thinking blog by Frank Harrell

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Drew LaMar onto Machine Learning