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 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)
Be a useful starting point for beginner pedagogy (both self-learning and teaching)
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
Be neither a checklist, nor a specification:
Comprehensive but non-exhaustive
General, not custom built for specific ML project types.
What we love
What we prefer
If … then …
Shame on you! (i.e. what we don't like)
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
Resonate with responsible ML best practice