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Working Groups/Zen of ML: Difference between revisions
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(Created page with "The Zen of Machine Learning was launched in the pilot cohort of '''Mozilla’s building trustworthy AI working group'''. The Zen of ML is a set of design principles that hel...") |
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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. | 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. | 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 Zen of ML should:'' | ''The Zen of ML should:'' | ||
Focus on decision making and identify decisions that arise in building and maintaining machine learning pipelines (from data collection to evaluation) | // Focus on decision making and identify decisions that arise in building and maintaining machine learning pipelines (from data collection to evaluation) | ||
Be a useful starting point for beginner pedagogy (both self-learning and teaching) | // Be a useful starting point for beginner pedagogy (both self-learning and teaching) | ||
Be a useful critical reflection tool to revisit for practitioners | // Be a useful critical reflection tool to revisit for practitioners | ||
Language must connect to the technical domain, but remain accessible and comprehensible - i.e. make sense to human beings, minimal jargon | // Language must connect to the technical domain, but remain accessible and comprehensible - i.e. make sense to human beings, minimal jargon | ||
Be neither a checklist, nor a specification: | // Be neither a checklist, nor a specification: | ||
Comprehensive but non-exhaustive | // Comprehensive but non-exhaustive | ||
General, not custom built for specific ML project types. | // General, not custom built for specific ML project types. | ||
What we love | // What we love | ||
What we prefer | // What we prefer | ||
If … then … | // If … then … | ||
Shame on you! (i.e. what we don't like) | // Shame on you! (i.e. what we don't like) | ||
Should not be specific to a particular framework (e.g. PyTorch, Scikit-learn). | // Should not be specific to a particular framework (e.g. PyTorch, Scikit-learn). | ||
Be as short as possible while being thorough; possible to process at a glance (or three). 18 - 20 short sentences are nice | // Be as short as possible while being thorough; possible to process at a glance (or three). 18 - 20 short sentences are nice | ||
Resonate with responsible ML best practice | // Resonate with responsible ML best practice | ||
The project held a workshop and hosted a hackathon at MozFest 2021. | The project held a workshop and hosted a hackathon at MozFest 2021. | ||
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[https://www.zenofml.org/ Website] | [https://www.zenofml.org/ Website] | ||
[https://miro.com/app/board/o9J_lSqB3rU=/ MozFest Hackathon Miro Board] password: zen202!ml | [https://miro.com/app/board/o9J_lSqB3rU=/ MozFest Hackathon Miro Board] password: zen202!ml | ||