ICT4S 2023 Community Workshop:



The development and use of “Artificial Intelligence” systems has diverse impacts on social and environmental sustainability [1]. While social issues such as the fairness of AI systems have been the focus of much public debate, the direct and indirect adverse environmental impacts (resource and energy use; increases in consumption induced by AI) have received less attention. To address multiple sustainability challenges in software and AI systems development several sustainability frameworks have been developed [2–8]. However, these frameworks seem to be known and applied by only a minority of software practitioners. With this workshop we explore several reasons why this might be the case by discussing with the participants:
  • how sustainability frameworks can work in practice in the context of AI systems development (e.g., how are indirect environmental impacts being measured and what are the system boundaries?),
  • who should be responsible for implementing the suggested frameworks (programmers, managers, researchers?),
  • why frameworks should be used (particularly in the absence of an obligation to do so),
  • how trade-offs between social, environmental and economic goals in frameworks can be addressed.

Objectives of the workshop

The objectives of this workshop are:
  • To connect actors (science, industry, policy) working in the areas of AI and social and environmental sustainability to enable joint learning about “sustainable AI” in a transdisciplinary dialogue,
  • To revisit existing frameworks for the development and use of sustainable software and sustainable AI with regards to their comprehensiveness and applicability,
  • To discuss challenges and opportunities related to the practical implementation and inherent trade-offs of frameworks of sustainable AI,
  • To identify next steps for research, joint outputs and possible collaborations across science and practice.


  • Duration: 3 hours
  • Number of participants: 15-20
  • Date: either Monday, June 5, or Friday, June 9, 2023.
  • Location: hybrid at ICT4S 2023 Rennes, France, and online, with a minimum 50 percent in person
  • Target group: researchers, industry representatives & policy makers working in the broad area of AI (also unrelated to sustainability) OR researchers working in the field of sustainability (in ICT/AI) OR both, where applicable.


  • Stefanie Kunkel (RIFS Potsdam)
  • Federica Lucivero (Wellcome Centre for Ethics and Humanities, University of Oxford)
  • Gabrielle Samuel (King’s College London)
  • Lucas Somavilla (University College London)
  • Carolyn Ten Holter (Responsible Technology Institute, University of Oxford)


  • [1] Kar AK, Choudhary SK, Singh VK. How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production 2022;376:134120. https://doi.org/10.1016/j.jclepro.2022.134120.
  • [2] Rohde F, Wagner J, Reinhard P, Petschow U. Nachhaltigkeitskriterien für künstliche Intelligenz: Entwicklung eines Kriterien- und Indikatorensets für die Nachhaltigkeitsbewertung von KI- Systemen entlang des Lebenszyklus. Berlin; 2021.
  • [3] European Commission. Study on the practical application of the new framework methodology for measuring the environmental impact of ICT – cost/benefit analysis: Final report. Publications Office; 2014.
  • [4] Kern E, Hilty LM, Guldner A, Maksimov YV, Filler A, Gröger J et al. Sustainable software products—Towards assessment criteria for resource and energy efficiency. Future Generation Computer Systems 2018;86:199–210.https://doi.org/10.1016/j.future.2018.02.044.
  • [5] Khalifeh A, Farrell P, Alrousan M, Alwardat S, Faisal M. Incorporating sustainability into software projects: a conceptual framework. IJMPB 2020;13(6):1339–61. https://doi.org/10.1108/IJMPB-12-2019-0289.
  • [6] Saputri TRD, Lee S-W. Integrated framework for incorporating sustainability design in software engineering life-cycle: An empirical study. Information and Software Technology 2021;129:106407.
  • [7] Genovesi S, Mönig JM. Acknowledging Sustainability in the Framework of Ethical Certification for AI. Sustainability 2022;14(7):4157. https://doi.org/10.3390/su14074157.
  • [8] Kaack LH, Donti PL, Strubell E, Kamiya G, Creutzig F, Rolnick D. Aligning artificial intelligence with climate change mitigation. Nat. Clim. Chang. 2022;12(6):518–27.https://doi.org/10.1038/s41558-022-01377-7