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Is Your AI-Based System Five Star Sustainable?

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Bridging the Gap Between AI and Reality (AISoLA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15217))

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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. 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.

References

  • Adams, R., et al.: Global index on responsible AI 2024 (2024)

    Google Scholar 

  • Anthony, L.F.W., Kanding, B., Selvan, R.: Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv preprint arXiv:2007.03051 (2020)

  • Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 610–623 (2021)

    Google Scholar 

  • Bommasani, R., Klyman, K., Kapoor, S., Longpre, S., Xiong, B., Maslej, N., Liang, P.: The foundation model transparency index v1. 1: May 2024. arXiv preprint arXiv:2407.12929 (2024)

  • Budennyy, S.A., et al.: Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable AI. In: Doklady Mathematics, vol. 106, Suppl. 1, pp. S118–S128. Springer (2022)

    Google Scholar 

  • Caballar, R.D.: We need to decarbonize software: the way we write software has unappreciated environmental impacts. IEEE Spectr. 61(4), 26–31 (2024)

    Article  MATH  Google Scholar 

  • CAIC: Centre for AI and climate (2024). https://www.c-ai-c.org/. Accessed 27 Aug 2024

  • Castaño, J., Martínez-Fernández, S., Franch, X., Bogner, J.: Exploring the carbon footprint of hugging face’s ml models: a repository mining study. In: 2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pp. 1–12. IEEE (2023)

    Google Scholar 

  • Cazzaniga, M., et al.: Gen-AI: artificial intelligence and the future of work. In: International Monetary Fund (2024)

    Google Scholar 

  • CCAI. Climate change AI (2024). https://www.climatechange.ai/. Accessed 27 Aug 2024

  • Climate-KIC, E.: Europe’s climate innovation agency (2024). https://www.climate-kic.org/. Accessed 27 Aug 2024

  • Crawford, K., Joler, V.: Anatomy of an AI system. In: Anatomy of an AI System (2018)

    Google Scholar 

  • CSIRO, A.N.S.A.: AI for clean energy and sustainability (2024). https://www.csiro.au/en/work-with-us/funding-programs/funding/Next-Generation-Graduates-Programs/Awarded-programs/AI-Clean-Energy-Sustainability. Accessed 27 Aug 2024

  • DIN. German standardization roadmap AI (2024). www.din.de/go/roadmap-ai. Accessed 29 Aug 2024

  • Falk, S., van Wynsberghe, A.: Challenging AI for sustainability: what ought it mean? AI and Ethics, pp. 1–11 (2023)

    Google Scholar 

  • Google: Ai for social good (2024). https://ai.google/responsibility/social-good/. Accessed 27 Aug 2024

  • GPAI: Global partnership on artificial intelligence (2024). https://gpai.ai/. Accessed 27 Aug 2024

  • Gupta, A., Lanteigne, C., Kingsley, S.: Secure: a social and environmental certificate for AI systems. arXiv preprint arXiv:2006.06217 (2020)

  • Gupta, A., Wright, C., Ganapini, M.B., Sweidan, M., Butalid, R.: The state of AI ethics report (volume 5). arXiv preprint arXiv:2108.03929 (2021)

  • Habuka, H.: Japan’s approach to ai regulation and its impact on the 2023 g7 presidency. Tech. rep., Center for Strategic and International Studies (CSIS) (2023). http://www.jstor.org/stable/resrep47347

  • Hankins, E., Fuentes Nettel, P., Martinescu, L., Grau, G., Rahim, S.: Government AI readiness index 2023 (2023). https://oxfordinsights.com/wp-content/uploads/2023/12/2023-Government-AI-Readiness-Index-2.pdf. Accessed 28 Aug 2024

  • Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., Pineau, J.: Towards the systematic reporting of the energy and carbon footprints of machine learning. J. Mach. Learn. Res. 21(248), 1–43 (2020)

    MathSciNet  Google Scholar 

  • Hilty, L.M., Hercheui, M.D.: ICT and sustainable development. In: IFIP International Conference on Human Choice and Computers, pp. 227–235. Springer (2010)

    Google Scholar 

  • IEEE. P7100 - standard for measurement of environmental impacts of artificial intelligence systems (2024). https://standards.ieee.org/ieee/7100/11671/. Accessed 29 Aug 2024

  • Kar, A.K., Choudhary, S.K., Singh, V.K.: How can artificial intelligence impact sustainability: a systematic literature review. J. Clean. Prod. 376, 134120 (2022)

    Article  MATH  Google Scholar 

  • Kazim, E., et al.: Innovation and opportunity: review of the UK’s national AI strategy. Discov. Artif. Intell. 1, 1–10 (2021)

    Google Scholar 

  • Kelly, B.: Ethical AI and the environment. iJournal: Stud. J. Facult. Inf. 7(2), 5–11 (2022)

    Article  MATH  Google Scholar 

  • Khowaja, S.A., Khuwaja, P., Dev, K., Wang, W., Nkenyereye, L.: ChatGPT needs SPADE (sustainability, privacy, digital divide, and ethics) evaluation: a review. Cognit. Comput. 1–23 (2024)

    Google Scholar 

  • Kolbert, E.: The obscene energy demands of AI. The New Yorker, March 9, 2024 (2024)

    Google Scholar 

  • Lee, T.B.: Is your linked open data 5 star? (2024). https://www.w3.org/DesignIssues/LinkedData.html. Accessed 29 Aug 2024

  • Luccioni, S., Jernite, Y., Strubell, E.: Power hungry processing: Watts driving the cost of AI deployment? In: The 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 85–99 (2024)

    Google Scholar 

  • Microsoft. Planetary computer (2024). https://planetarycomputer.microsoft.com/. Accessed 27 Aug 2024

  • MLCommons. Mlcommons (2024). https://mlcommons.org/. Accessed 28 Aug 2024

  • OECD. Measuring the environmental impacts of artificial intelligence compute and applications (2022). https://www.oecd-ilibrary.org/content/paper/7babf571-en. Accessed 27 Aug 2024

  • Park, E.: The AI bill of rights: a step in the right direction. Orange County Lawyer Magazine 65(2) (2023)

    Google Scholar 

  • Parliament, E.: Artificial intelligence act. (2024). https://www.europarl.europa.eu/doceo/document/TA-9-2024-0138_EN.html. Accessed 29 Aug 2024

  • Patterson, D., et al.: Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350 (2021)

  • Perrault, R., Clark, J.: Artificial Intelligence Index Report 2024. Stanford University, Tech. rep. (2024)

    MATH  Google Scholar 

  • Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)

  • Reddi, V.J., et al.: Mlperf inference benchmark. In: 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), pp. 446–459. IEEE (2020)

    Google Scholar 

  • Roberts, H., Cowls, J., Morley, J., Taddeo, M., Wang, V., Floridi, L.: The Chinese Approach to Artificial Intelligence: An Analysis of Policy, Ethics, and Regulation, pp. 47–79. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-81907-1_5. ISBN 978-3-030-81907-1

  • Rohde, F., et al.: Broadening the perspective for sustainable artificial intelligence: sustainability criteria and indicators for artificial intelligence systems. Curr. Opin. Environ. Sustain. 66, 101411 (2024)

    Google Scholar 

  • Saari, L., Kuusisto, O., Pirttikangas, S.: AI maturity web tool helps organisations proceed with AI (2019)

    Google Scholar 

  • Scassa, T.: Administrative law and the governance of automated decision-making: a critical look at Canada’s directive on automated decision-making. Forthcoming (2021) 54(1) (2020)

    Google Scholar 

  • Schieferdecker, I.: Climate change and AI. A research agenda for sustainable intelligence. In: Kox, T., Ullrich, A., Zech, H. (eds.) 6th Weizenbaum Conference. Uncertain Journeys into Digital Futures, p. 17 (2024)

    Google Scholar 

  • Schwartz, R., Dodge, J., Smith, N.A., Etzioni, O.: Green AI. Commun. ACM 63(12), 54–63 (2020)

    Article  Google Scholar 

  • Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for modern deep learning research. Proc. AAAI Conf. Artif. Intell. 34(09), 13693–13696 (2020)

    Google Scholar 

  • Thomas, R., Uminsky, D.: The problem with metrics is a fundamental problem for AI. arXiv preprint arXiv:2002.08512 (2020)

  • Wang, A., et al.: Superglue: a stickier benchmark for general-purpose language understanding systems. Adv. Neural Inf. Process. Syst. 32 (2019)

    Google Scholar 

  • Wang, Q., Li, Y., Li, R.: Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI). Human. Soc. Sci. Commun. 11(1), 1–18 (2024)

    Google Scholar 

  • Wörsdörfer, M.: Mitigating the adverse effects of AI with the European union’s artificial intelligence act: hype or hope? Glob. Bus. Organ. Excell. 43(3), 106–126 (2024)

    Article  Google Scholar 

  • Yams, N.B., Richardson, V., Shubina, G.E., Albrecht, S., Gillblad, D.: Integrated AI and innovation management: the beginning of a beautiful friendship. Technol. Innov. Manag. Rev. 10(11) (2020)

    Google Scholar 

  • Yeung, K.: Recommendation of the council on artificial intelligence (OECD). Int. Leg. Mater. 59(1), 27–34 (2020)

    Article  MATH  Google Scholar 

Download references

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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Correspondence to Ina K. Schieferdecker .

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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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  • DOI: https://doi.org/10.1007/978-3-031-75434-0_1

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