Working Groups/Zen of ML: Difference between revisions

This project is collecting, refining, publishing, and disseminating  the Zen of ML design principles so that machine learning (ML) practitioners can develop and deploy ML code responsibly.

Machine learning (ML) tools are freely and widely available, and can be accessed with simple API calls and standard development pipelines. This has made it possible for anybody who wants to use ML to learn the skills and access the tools to do so. When using ML as a code component, the data-dependent and probabilistic nature of its outputs is hidden and often overlooked. This can have undesirable and even harmful consequences. Thus, while democratising ML tools has increased the inclusiveness of ML, it has created a new challenge as the responsible use of ML tools cannot be guaranteed or controlled. This presents a particular risk for people that are adversely affected by the outcomes of decisions backed by ML predictions.

A list of statements that can be disseminated to ML educators and self-learners to embed the responsible development of ML artifacts and products into the design cycle from the get-go.

We draw inspiration from the Zen of Python, which can be accessed from any python shell with import this. Similarly, we would like to see the Zen of ML included as an import statement in scikit-learn, the entry point to machine learning for many people.

The project held a workshop and hosted a hackathon at MozFest 2021.

Andy Forest, Bernease Herman, Borhane Blili-Hamelin, Dessalegn Yehuala, Gaurav Jain, Jenine Carron, Jessica Zhou, Kyle Smith, Kyle Meenehan, Vanja Skoric

[https://www.zenofml.org/ Website]   

[https://miro.com/app/board/o9J_lSqB3rU=/ MozFest Hackathon Miro Board] password: zen202!ml