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Paper Type
Complete
Description
AI-based solutions show great potential in various fields, including the context of sustainability. In light of the great potential, it is often overlooked that advances in performance come at a significant cost to the environment, as training data- and computation-intensive models involves high carbon emissions. Climate change and its increasing awareness are forcing companies to use available resources more efficiently, which for the field of AI means developing accurate models in an energy-aware manner. We conduct a systematic literature review on approaches for sustainable AI development and organize the existing knowledge along the phases of the established CRISP-DM model. In this way, we provide managers and developers with a holistic picture of opportunities for reducing the environmental footprint in all phases of typical enterprise AI projects.
Paper Number
1468
Recommended Citation
Müller, Kristina; Kröckel, Pavlina; and Bodendorf, Freimut, "Sustainable CRISP-DM Extension for Energy-Aware AI Development" (2023). AMCIS 2023 Proceedings. 9.
https://aisel.aisnet.org/amcis2023/sig_green/sig_green/9
Sustainable CRISP-DM Extension for Energy-Aware AI Development
AI-based solutions show great potential in various fields, including the context of sustainability. In light of the great potential, it is often overlooked that advances in performance come at a significant cost to the environment, as training data- and computation-intensive models involves high carbon emissions. Climate change and its increasing awareness are forcing companies to use available resources more efficiently, which for the field of AI means developing accurate models in an energy-aware manner. We conduct a systematic literature review on approaches for sustainable AI development and organize the existing knowledge along the phases of the established CRISP-DM model. In this way, we provide managers and developers with a holistic picture of opportunities for reducing the environmental footprint in all phases of typical enterprise AI projects.
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