Sustainable CRISP-DM Extension for Energy-Aware AI Development

Müller K, Kröckel P, Bodendorf F (2023)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Conference Proceedings Title: AMCIS 2023 Proceedings

Event location: Panama City PA

URI: https://aisel.aisnet.org/amcis2023/sig_green/sig_green/9

Abstract

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.

Authors with CRIS profile

Kristina Müller Department of Data Science Pavlina Kröckel Lehrstuhl für Wirtschaftsinformatik, insbesondere im Dienstleistungsbereich Freimut Bodendorf Department of Data Science

How to cite

APA:

Müller, K., Kröckel, P., & Bodendorf, F. (2023). Sustainable CRISP-DM Extension for Energy-Aware AI Development. In AMCIS 2023 Proceedings. Panama City, PA.

MLA:

Müller, Kristina, Pavlina Kröckel, and Freimut Bodendorf. "Sustainable CRISP-DM Extension for Energy-Aware AI Development." Proceedings of the AMCIS 2023, Panama City 2023.

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