  Automated machine learning (AutoML) strives for the automatic configuration
of machine learning algorithms and their composition into an overall (software)
solution - a machine learning pipeline - tailored to the learning task
(dataset) at hand. Over the last decade, AutoML has developed into an
independent research field with hundreds of contributions. At the same time,
AutoML is being criticised for its high resource consumption as many approaches
rely on the (costly) evaluation of many machine learning pipelines, as well as
the expensive large scale experiments across many datasets and approaches. In
the spirit of recent work on Green AI, this paper proposes Green AutoML, a
paradigm to make the whole AutoML process more environmentally friendly.
Therefore, we first elaborate on how to quantify the environmental footprint of
an AutoML tool. Afterward, different strategies on how to design and benchmark
an AutoML tool wrt. their "greenness", i.e. sustainability, are summarized.
Finally, we elaborate on how to be transparent about the environmental
footprint and what kind of research incentives could direct the community into
a more sustainable AutoML research direction. Additionally, we propose a
sustainability checklist to be attached to every AutoML paper featuring all
core aspects of Green AutoML.

Machine Learning