Artificial Intelligence and Sustainability: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Rachit Dhiman
,
Sofia Miteff
,
Yuancheng Wang
,
Shih-Chi Ma
,
Ramila Amirikas
,
Benjamin Fabian

Artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This Systematic Mapping Study (SMS) study accomplishes a comprehensive analysis of "AI Sustainability," integrating both the sustainability of AI and AI for sustainability across environmental, social, and economic dimensions. The field exhibits a dynamic landscape, maturing significantly since 2019 with a surge in publications and diverse contributions. The study reveals a balanced perspective, emphasizing both sustainability perspectives equally. Recent papers indicate a trend towards holistic studies, yet the economic dimension remains relatively underexplored. Future research is encouraged to delve into the economic dimension, align with the United Nations’ Sustainable Development Goals (SDGs), and address stakeholder influence, ensuring a sustainable and inclusive AI future.

  • artificial intelligence
  • AI
  • sustainability
  • systematic mapping study

1. Introduction

In the past decades, seminal advancements have been made in the field of Artificial Intelligence (AI) [1]. AI has the potential to transform various markets and industries, driving unforeseen change [2][3]. Sectors such as healthcare [4], transport [5], agriculture [6], energy [7], and the media [8] have seen major changes implemented as a result of AI systems. Despite widespread enthusiasm, there exists a notable caution, stemming from both evidence showcasing AI’s efficacy and concerns about potential negative effects [9]. For instance, training a state-of-the-art model, especially Natural Language Processing (NLP) models, requires substantial computational resources, imposing significant energy along with associated financial and environmental costs [10]. Furthermore, the rise of AI also sparked new ethical and societal challenges for the economy and society. These challenges include concerns about stagnant real wages for workers [11] and social injustice originating from discriminating AI systems [12][13], as well as the proliferation of fake news [14][15][16]. Hence, researchers are increasingly interested in examining their impact on sustainability. Comprehending the effects and transformative potential AI can drive, specifically on sustainability, requires a critical review of the topic.
Sustainability can be analyzed by focusing on one of the following three dimensions: economic, social, and environmental [17]. Furthermore, when surveying the current body of literature on AI sustainability, research is often divided between AI as a tool for achieving sustainable goals and the impact of AI on sustainability [18]. However, current studies on this topic often look only at one certain dimension, which might oversimplify the issue at hand, creating a narrow view of what AI sustainability truly entitles.
Oxford dictionary defines AI as “the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages”. Here, we see the emergence of AI as a system that can act similarly to humans. John McCarthy, widely known as one of the fathers of AI, defined AI as “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable” [19]. Though in McCarthy’s definition, the idea of human-like behavior is present, he goes further by defying the limits of the possibility of AI, differentiating it from human intelligence, and highlighting that its boundaries are not limited by biology.
When it comes to sustainability, we can see an evolution of the concept. Early on, in 1987, the United Nations Brundtland Commission defined sustainability as “meeting the needs of the present without compromising the ability of future generations to meet their own needs”. Authors have since added depth to the concept, breaking it down into three different dimensions or pillars: social, economic, and environmental [17].
Ref. [18] points out the dichotomy of using AI to achieve sustainability and sustainability of utilizing AI systems, and hence, the term “Sustainable AI” has been introduced to address the whole socio-technical system of AI.
According to existent literature, the rapid growth of AI has raised concerns about its environmental and social sustainability [20]. To address these issues, there is a need for AI governance and regulation, multidisciplinary collaboration, and building trust in AI applications [21]. AI has the potential to support environmental governance and reduce resource and energy intensity [22]. However, there are challenges, such as over-reliance on historical data, uncertain human behavioral responses, and cybersecurity risks [22]. Ref. [22] also suggests that future research should consider multilevel views, systems dynamics approaches, design thinking, psychological and sociological considerations, and economic value considerations.

2. AI for Sustainability

The study by [22] argues that AI has the potential to facilitate the development of culturally suitable organizational processes and individual practices that can effectively reduce the ecological footprint of human activities. However, the true significance of AI lies not solely in its capacity to diminish energy, water, and land usage intensities within society, but rather in its ability to enhance and nurture environmental governance at a higher level.
Another literature review of AI for sustainability by Kar et al. [23] focuses on diverse applications across sectors such as construction, transportation, healthcare, manufacturing, agriculture, and water management. The research highlights the approaches, difficulties, and obstacles regarding adopting AI for sustainable development.
This literature highlights the various methods used to improve sustainable practices on a small to large scale using AI and various future research directions for academic researchers [23].
In short, the state-of-the-art research on AI for sustainability encompasses a wide range of topics, including energy efficiency, climate change mitigation, resource management, biodiversity conservation, and more.

3. Sustainability of AI

The review paper by Verdecchia et al. [24] reviews the growing field of Green AI, which addresses the environmental impact of artificial intelligence. It highlights trends like increased interest since 2020, methods for improving model sustainability, and the involvement of academia and industry. The findings suggest a mature field ready for broader adoption in both research and industrial practice.
The study by Natarajan et al. [25] has the main aim of discerning the ongoing research trajectories within the intersection of AI and sustainability. Additionally, the article employs the affordance theory as its conceptual framework, intending to pinpoint the affordances within the realm of sustainable AI.
Notably, the field has exhibited significant growth since 2020. The majority of studies focus on monitoring AI model footprints, fine-tuning hyperparameters to enhance model sustainability, or conducting model benchmarking.

4. Combining AI for Sustainability and Sustainability of AI

While the applications of AI and related technologies have the potential for more efficient utilization of land and seascapes, heightened capabilities in environmental monitoring, and enhanced transparency within supply chains, there could also be systemic sustainability challenges emerging as these AI technologies extend to novel social, economic, and ecological domains. Although some recent compilations [18][26] briefly acknowledge these risks, they often provide only brief elaboration on the potential harms and unanticipated social and ecological consequences [27]. In many cases, influential reports outlining the societal impacts of AI either disregard the dimensions of sustainability entirely or downplay the conceivable social, economic, and ecological risks they might pose [28]. In contrast, ref. [29] offers a more holistic overview of the involvement of those technologies in fields which have a relatively greater influence on sustainability in an environmental sense. The study also addresses potential challenges that could jeopardize the sustainability of AI. Besides the unfolding of these underlying challenges, the authors also discuss the limitations of existing research frameworks in effectively tackling sustainability-related AI risks within these sectors [29].
Future research endeavors are encouraged to delve more into the economic dimension, aligning their goals with the United Nations’ Sustainable Development Goals, thereby providing promising avenues for further exploration and contributing to the holistic development of the field. Moreover, it is imperative to acknowledge and address concerns  regarding the potential influence of stakeholders on AI development and its implications for sustainable contributions. Safeguarding consumers’ interests and safety is of high importance, especially given the challenges in tracking potentially problematic AI decisions and the potential lack of access to evidence for affected individuals.

This entry is adapted from the peer-reviewed paper 10.3390/analytics3010008

References

  1. Russell, S.; Norvig, P. Artificial Intelligence: A Modern Approach, 4th ed.; Pearson Education Limited: Oxford, UK, 2021.
  2. Davenport, T.H.; Ronanki, R. Artificial intelligence for the real world. Harv. Bus. Rev. 2018, 96, 108–116.
  3. Enholm, I.M.; Papagiannidis, E.; Mikalef, P.; Krogstie, J. Artificial Intelligence and Business Value: A Literature Review. Inf. Syst. Front. 2022, 24, 1709–1734.
  4. Lee, D.; Yoon, S.N. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int. J. Environ. Res. Public Health 2021, 18, 271.
  5. Abduljabbar, R.; Dia, H.; Liyanage, S.; Bagloee, S.A. Applications of Artificial Intelligence in Transport: An Overview. Sustainability 2019, 11, 189.
  6. Jha, K.; Doshi, A.; Patel, P.; Shah, M. A comprehensive review on automation in agriculture using artificial intelligence. Artif. Intell. Agric. 2019, 2, 1–12.
  7. Ahmad, T.; Zhang, D.; Huang, C.; Zhang, H.; Dai, N.; Song, Y.; Chen, H. Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. J. Clean. Prod. 2021, 289, 125834.
  8. Chan-Olmsted, S.M. A Review of Artificial Intelligence Adoptions in the Media Industry. Int. J. Media Manag. 2019, 21, 193–215.
  9. Sætra, H.S. AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System. Sustainability 2021, 13, 1738.
  10. Strubell, E.; Ganesh, A.; McCallum, A. Energy and Policy Considerations for Deep Learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 28 July–2 August 2019; pp. 3645–3650.
  11. Santor, E. The Impact of Digitalization on the Economy: A Review Article on the NBER Volume “Economics of Artificial Intelligence: An Agenda”. Int. Product. Monit. 2020, 39, 81–90.
  12. Dastin, J. Amazon scraps secret AI recruiting tool that showed bias against women. In Ethics of Data and Analytics; Auerbach Publications: Boca Raton, FL, USA, 2022; pp. 296–299.
  13. Larson, J.; Angwin, J.; Kirchner, L.; Mattu, S. Machine Bias: There’s Software Used across the Country to Predict Future Criminals. And Its Biased against Blacks. Fed. Probat. 2016, 80, 38.
  14. Jin, Z.; Cao, J.; Guo, H.; Zhang, Y.; Wang, Y.; Luo, J. Detection and analysis of 2016 us presidential election related rumors on twitter. In Proceedings of the Social, Cultural, and Behavioral Modeling: 10th International Conference, SBP-BRiMS 2017, Washington, DC, USA, 5–8 July 2017; Proceedings 10. Springer: Berlin/Heidelberg, Germany, 2017; pp. 14–24.
  15. Bovet, A.; Makse, H.A. Influence of fake news in Twitter during the 2016 US presidential election. Nat. Commun. 2019, 10, 7.
  16. Krittanawong, C.; Narasimhan, B.; Virk, H.U.H.; Narasimhan, H.; Hahn, J.; Wang, Z.; Tang, W.W. Misinformation dissemination in twitter in the COVID-19 era. Am. J. Med. 2020, 133, 1367–1369.
  17. Purvis, B.; Mao, Y.; Robinson, D. Three Pillars of Sustainability: In Search of Conceptual Origins. Sustain. Sci. 2019, 14, 681–695.
  18. Van Wynsberghe, A. Sustainable AI: AI for Sustainability and the Sustainability of AI. AI Ethics 2021, 1, 213–218.
  19. McCarthy, J. What is Artificial Intelligence? 2007. Available online: http://www-formal.stanford.edu/jmc/whatisai/ (accessed on 31 August 2023).
  20. Chen, Z.; Wu, M.; Chan, A.; Li, X.; Ong, Y.S. Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges . IEEE Comput. Intell. Mag. 2023, 18, 60–77.
  21. Larsson, S.; Anneroth, M.; Felländer, A.; Felländer-Tsai, L.; Heintz, F.; Ångström, R. Sustainable AI: An Inventory of the State of Knowledge of Ethical, Social, and Legal Challenges Related to Artificial Intelligence. AI Sustainability Center. 2019. Available online: https://www.lunduniversity.lu.se/lup/publication/e2fa1b6a-860e-44b0-a359-fbd842c363db (accessed on 31 August 2023).
  22. Nishant, R.; Kennedy, M.; Corbett, J. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. Int. J. Inf. Manag. 2020, 53, 102104.
  23. Kar, A.K.; Choudhary, S.K.; Singh, V.K. How Can Artificial Intelligence Impact Sustainability: A Systematic Literature Review. J. Clean. Prod. 2022, 376, 134120.
  24. Verdecchia, R.; Sallou, J.; Cruz, L. A Systematic Review of Green AI. arXiv 2023, arXiv:2301.11047.
  25. Natarajan, H.K.; de Paula, D.; Dremel, C.; Uebernickel, F. A Theoretical Review on AI Affordances for Sustainability. In Proceedings of the AMCIS 2022, Minneapolis, MN, USA, 10–14 August 2022; Available online: https://www.alexandria.unisg.ch/server/api/core/bitstreams/d8858182-3dfb-492e-b3a3-57c336fc7092/content (accessed on 31 August 2023).
  26. Wearn, O.R.; Freeman, R.; Jacoby, D.M.P. Responsible AI for Conservation. Nat. Mach. Intell. 2019, 1, 72–73.
  27. Galaz, V.; Mouazen, A.M. ‘New Wilderness’ Requires Algorithmic Transparency: A Response to Cantrell et al. Trends Ecol. Evol. 2017, 32, 628–629.
  28. Joppa, L.N. The Case for Technology Investments in the Environment. Nature 2017, 552, 325–328.
  29. Galaz, V.; Centeno, M.A.; Callahan, P.W.; Causevic, A.; Patterson, T.; Brass, I.; Baum, S.; Farber, D.; Fischer, J.; Garcia, D.; et al. Artificial Intelligence, Systemic Risks, and Sustainability. Technol. Soc. 2021, 67, 101741.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback