Towards Green Automated Machine Learning: Status Quo and Future Directions

Abstract

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.

Published in Journal of Artificial Intelligence Research

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Cited by in corpus (4)

• Towards Green Automated Machine Learning: Status Quo and Future Directions
• Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechia
• Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)
• Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts