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CRISP-ML(QS): Embedding Sustainability in AI-ML Development Lifecycle
- Truvace record
- TRV-2026-2542raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- 3fc6770e245d…check it
Artificial Intelligence (AI) in contemporary industry and society is omnipresent, with its applications permeating across enterprise functions such as manufacturing, supply chain and research & development, and enterprise goals such as efficiency, innovation, and sustainability. However, an important question that has been raised, quite often recently, is: Is AI environmentally sustainable? This study proposes an extension to the CRoss-Industry Standard Process for Machine Learning with Quality Assurance (CRISP-…
The gain
In a review of 171 papers, studies it cites report that smaller training datasets cut machine-learning energy use by as much as 92% and simpler models cut energy and emissions by 23% to 99% at similar accuracy.
What this doesn’t fix
The framework covers only the environmental dimension and software-level choices, and the savings come from separate studies rather than one controlled test; effects differ by algorithm.
Sources
1 on file- International Journal of Mathematical, Engineering and Management Sciencesdoi:10.33889/ijmems.2026.11.5.083
Cite this record
v1Truvace Impact Record TRV-2026-2542, v1: “CRISP-ML(QS): Embedding Sustainability in AI-ML Development Lifecycle.” Truvace, 2026-11-12. /record/TRV-2026-2542 (accessed on the day you copy it). sha256 3fc6770e245d7325…
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1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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