Abstract
Artificial Intelligence (AI) is one of the most powerful tools available to humanity. While some debate the potential dangers of AI in general and consider a moratorium on AI development and applications necessary, others propose concepts such as AI for Sustainability and Sustainable AI to harness the potential of AI to address humanity’s most pressing challenges, including climate change to combat environmental degradation - in short, Sustainable Intelligence. In order to ground the still heated debate, this paper delves deeper into the state of the art of Sustainable Intelligence, in particular how to qualitatively and quantitatively explore the environmental impact of AI-based systems and how to minimise it. It also discusses selected research topics that need to be further explored and concludes with a Rising Star system for Sustainable Intelligence to share knowledge on environmental and societal goals for AI-based systems and to report and optimise their qualitative indicators and quantitative metrics in an open and standardised way.
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Notes
- 1.
The Global Partnership on Artificial Intelligence (GPAI GPAI (2024)), launched by the OECD in 2020, is an international initiative to guide the responsible development and use of AI in a way that respects human rights, inclusivity, diversity, innovation and economic growth.
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Acknowledgments
The ideas presented in this paper have been developed through constructive dialogue with colleagues and associates at the Weizenbaum Institute for the Networked Society, the German Advisory Council on Global Change, and the Federal Ministry of Education and Research. The author acknowledges that while authored by her, the writing process was aided by AI tools, specifically ResearchRabbit for determining related work, and ChatGPT and DeepL for fine-tuning the wording.
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Schieferdecker, I.K. (2025). Is Your AI-Based System Five Star Sustainable?. In: Steffen, B. (eds) Bridging the Gap Between AI and Reality. AISoLA 2024. Lecture Notes in Computer Science, vol 15217. Springer, Cham. https://doi.org/10.1007/978-3-031-75434-0_1
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