The Hitchhiker’s Guide to Responsible Machine Learning

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Yesterday Olga Tokarczuk (2018 Nobel Prize in Literature) said in an interview that when she thinks about literature, she no longer thinks about books!!!

So, how should we effectively tell the most important story in predictive modelling i.e. The story of Responsible ML?

We (MI2DataLab) are currently working on an exciting and interdisciplinary experiment combining a classic textbook with a comic book, combining a description of methods and software with a description of process, combining a description of a specific use-case about COVID-19 data analysis with universal best practices.

These 52 page long teaching materials describe how to build a predictive model, compare the developed models, and use XAI to analyze them, plus a bonus — how to deploy model with explanations in a similar form to

The material is prepared as a starter for predictive modelling. The included code examples can be executed and experimented with on your own (the first version has examples in R, but there will be albo translation for Python). No prior knowledge in machine learning is required, the materials should be readable and interesting both for a high school student interested in data analysis and for experienced analysts who are curious about what is new in predictive modelling.

It is scheduled for release this fall, so follow us on medium to get access to it as soon as it is public!

If you are interested in other posts about explainable, fair, and responsible ML, follow #ResponsibleML on Medium.

In order to see more R related content visit

The Hitchhiker’s Guide to Responsible Machine Learning was originally published in ResponsibleML on Medium, where people are continuing the conversation by highlighting and responding to this story.

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