SavedAdd your journal

Journals / Sakarya Üniversitesi İşletme Enstitüsü Dergisi / 2026 / Cilt: 8 Sayı: 1

A Systematic Review Integrating the Perspectives of Artificial Intelligence for Sustainability and Sustainability of Artificial Intelligence

Taner Turgut(SAKARYA UNIVERSITY, FACULTY OF BUSINESS ADMINISTRATION, DEPARTMENT OF MANAGEMENT INFORMATION SYSTEMS, MANAGEMENT INFORMATION SYSTEMS PR.)0000-0002-3084-7515
Muhammed Doğramacı(SAKARYA UNIVERSITY, FACULTY OF BUSINESS ADMINISTRATION, DEPARTMENT OF MANAGEMENT INFORMATION SYSTEMS, MANAGEMENT INFORMATION SYSTEMS PR.)0000-0002-8819-8802
Selis Güler Siler(SAKARYA UNIVERSITY, FACULTY OF BUSINESS ADMINISTRATION, DEPARTMENT OF MANAGEMENT INFORMATION SYSTEMS, MANAGEMENT INFORMATION SYSTEMS PR.)0009-0000-2430-1295
Büşra Alma Çallı(SAKARYA UNIVERSITY, FACULTY OF BUSINESS ADMINISTRATION, DEPARTMENT OF MANAGEMENT INFORMATION SYSTEMS, MANAGEMENT INFORMATION SYSTEMS PR.)0000-0001-7411-4295
Pages
40–59
Publication Date
2026-06-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings point to three priorities: advancing empirical research on social sustainability, integrating a lifecycle perspective into ecological analyses, and examining the adaptability of governance models across diverse regional contexts.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:While artificial intelligence technologies offer significant opportunities for achieving the Sustainable Development Goals (SDGs), they also present ethical, environmental, and economic challenges.
📊
Key Findings & Data:This study provides a comprehensive systematic literature review that maps the dimensions of “AI for Sustainability” and “Sustainability of AI” within an integrated theoretical framework.
💡
Conclusion & Impact:The findings point to three priorities: advancing empirical research on social sustainability, integrating a lifecycle perspective into ecological analyses, and examining the adaptability of governance models across diverse regional contexts.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Intelligence · Sustainability · Sustainable AI

Abstract

While artificial intelligence technologies offer significant opportunities for achieving the Sustainable Development Goals (SDGs), they also present ethical, environmental, and economic challenges. This study provides a comprehensive systematic literature review that maps the dimensions of “AI for Sustainability” and “Sustainability of AI” within an integrated theoretical framework. Using the PRISMA methodology, 167 records were compiled from Web of Science, Scopus, and Google Scholar; after applying inclusion and exclusion criteria, 81 articles were analysed in depth. Studies focused primarily on engineering and technical computation were excluded to ensure alignment with the study’s social sciences and sustainability management orientation. Findings in the “AI for Sustainability” dimension reveal that AI makes significant contributions to SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). However, empirical research on SDG 5 (Gender Equality), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action) remains insufficient, with social and environmental SDGs systematically underrepresented. In the “Sustainability of AI” dimension, the literature predominantly focuses on the ecological sub-theme (energy consumption and carbon emissions), while the organisational, social, and economic dimensions remain comparatively neglected. Notable findings include the context-dependent nature of energy-efficiency strategies and evidence that current AI infrastructure creates an unsustainable carbon lock-in. This study offers a thematic and normative mapping that addresses the limitations of prior reviews in integrating both dimensions. The findings point to three priorities: advancing empirical research on social sustainability, integrating a lifecycle perspective into ecological analyses, and examining the adaptability of governance models across diverse regional contexts.

🔖

Cite This Article

Turgut, T., Doğramacı, M., Siler, S. G., Çallı, B. A. (2026). A Systematic Review Integrating the Perspectives of Artificial Intelligence for Sustainability and Sustainability of Artificial Intelligence. Sakarya Üniversitesi İşletme Enstitüsü Dergisi, Cilt: 8 Sayı: 1, 40–59. https://doi.org/10.47542/sauied.1850659

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (77)

  1. Abulibdeh, A., Zaidan, E., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production, 437. https://doi.org/10.1016/j.jclepro.2023.140527
  2. Adewale, B. A., Ene, V. O., Ogunbayo, B. F., & Aigbavboa, C. O. (2024). A systematic review of the applications of AI in a sustainable building’s lifecycle. Buildings, 14(7), Article 2137. https://doi.org/10.3390/buildings14072137
  3. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production, 289, Article 125834. https://doi.org/10.1016/j.jclepro.2021.125834
  4. Ali, S., Fapi, E. T., Jaumard, B., & Planche, A. (2024). Focus on Carbon Dioxide Footprint of AI/ML Model Training. 524–529. https://doi.org/10.1109/imsa61967.2024.10652803
  5. Alshahrani, R., Yenugula, M., Algethami, H., Alharbi, F., Goswami, S. S., Naveed, Q. N., Lasisi, A., Islam, S., Khan, N. A., & Zahmatkesh, S. (2024). Establishing the fuzzy integrated hybrid MCDM framework to identify the key barriers to implementing artificial intelligence-enabled sustainable cloud system in an IT industry. Expert Systems with Applications, 238. https://doi.org/10.1016/j.eswa.2023.121732
  6. Bolón-Canedo, V., Morán-Fernández, L., Cancela, B., & Alonso-Betanzos, A. (2024). A review of green artificial intelligence: Towards a more sustainable future. Neurocomputing, 599. https://doi.org/10.1016/j.neucom.2024.128096
  7. Bolte, L., Vandemeulebroucke, T., & Wynsberghe, A. van. (2022). From an Ethics of Carefulness to an Ethics of Desirability: Going Beyond Current Ethics Approaches to Sustainable AI. Sustainability, 14(8). https://doi.org/10.3390/su14084472
  8. Bossert, L. N., & Hagendorff, T. (2023). The ethics of sustainable AI: Why animals (should) matter for a sustainable use of AI. Sustainable Development, 31(5), 3459–3467. https://doi.org/10.1002/sd.2596
  9. Bracarense, N., Bawack, R. E., Wamba, S. F., & Carillo, K. D. A. (2022). Artificial intelligence and sustainability: A bibliometric analysis and future research directions. Pacific Asia Journal of the Association for Information Systems, 14(2), 136–159. https://doi.org/10.17705/1pais.14209
  10. Budennyy, S. A., Lazarev, V. D., Zakharenko, N. N., Korovin, A. N., Plosskaya, O. A., Dimitrov, D. V., Akhripkin, V. S., Pavlov, I. V., Oseledets, I. V., Barsola, I. S., Egorov, I. V., Kosterina, A. A., & Zhukov, L. E. (2022). eco2AI: Carbon Emissions Tracking of Machine Learning Models as the First Step Towards Sustainable AI. Doklady Mathematics, 106, S118–S128. https://doi.org/10.1134/S1064562422060230
  11. Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A. A. (2024). Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability, 16(21), Article 9145. https://doi.org/10.3390/su16219145
  12. Dai, S., Luo, Z., Luo, W., Wang, S., Dai, C., Guo, B., & Zhou, X. (2024). Energy-Efficient Inference with Software-Hardware Co-Design for Sustainable Artificial Intelligence of Things. IEEE Internet of Things Journal, 11(24), 39170-39182. https://doi.org/10.1109/JIOT.2024.3482288
  13. Durlik, I., Miller, T., Kostecka, E., Łobodzińska, A., & Kostecki, T. (2024). Harnessing AI for sustainable shipping and green ports: Challenges and opportunities. Applied Sciences, 14(14), Article 5994. https://doi.org/10.3390/app14145994
  14. Elmousalami, H., Elshaboury, N., & Elyamany, A. H. (2024). Green artificial intelligence for cost-duration variance prediction (CDVP) for irrigation canals rehabilitation projects. Expert Systems with Applications, 249. https://doi.org/10.1016/j.eswa.2024.123789
  15. Escarda, M., Eiras-Franco, C., Cancela, B., Guijarro-Berdiñas, B., & Alonso-Betanzos, A. (2025). Performance and sustainability of BERT derivatives in dyadic data. Expert Systems with Applications, 262. https://doi.org/10.1016/j.eswa.2024.125647
  16. Falk, S., Wynsberghe, A. van, & Biber-Freudenberger, L. (2024). The attribution problem of a seemingly intangible industry. Environmental Challenges, 16, Article 101003. https://doi.org/10.1016/j.envc.2024.101003
  17. Fernandez-Aller, C., Velasco, A. F. D., Manjarres, A., Pastor-Escuredo, D., Pickin, S., Criado, J. S., & Ausin, T. (2021). An Inclusive and Sustainable Artificial Intelligence Strategy for Europe Based on Human Rights. IEEE Technology and Society Magazine, 40(1), 46–54. https://doi.org/10.1109/MTS.2021.3056283
  18. Ferro, M., Silva, G. D., Paula, F. B. de, Vieira, V., & Schulze, B. (2023). Towards a sustainable artificial intelligence: A case study of energy efficiency in decision tree algorithms. Concurrency and Computation: Practice and Experience, 35(17). https://doi.org/10.1002/cpe.6815
  19. Gill, A. S., & Germann, S. (2022). Conceptual and normative approaches to AI governance for a global digital ecosystem supportive of the UN Sustainable Development Goals (SDGs). AI and Ethics, 2(2), 293–301. https://doi.org/10.1007/s43681-021-00058-z
  20. Gupta, B. B., Gaurav, A., Panigrahi, P. K., & Arya, V. (2023). Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship. Technological Forecasting and Social Change, 186. https://doi.org/10.1016/j.techfore.2022.122152
  21. Halsband, A. (2022). Sustainable AI and Intergenerational Justice. Sustainability, 14(7). https://doi.org/10.3390/su14073922
  22. Isensee, C., Griese, K.-M., & Teuteberg, F. (2021). Sustainable artificial intelligence: A corporate culture perspective. Sustainability Management Forum | Nachhaltigkeits Management Forum, 29(3–4), 217–230. https://doi.org/10.1007/s00550-021-00524-6
  23. Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 376, Article 134120. https://doi.org/10.1016/j.jclepro.2022.134120
  24. Kindylidi, I., & Cabral, T. S. (2021). Sustainability of ai: The case of provision of information to consumers. Sustainability, 13(21). https://doi.org/10.3390/su132112064
  25. Kopka, A., & Grashof, N. (2022). Artificial intelligence: Catalyst or barrier on the path to sustainability? Technological Forecasting and Social Change, 175.
  26. Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024). Artificial intelligence—Driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals. Sustainable Development, 32(3), 2253–2267. https://doi.org/10.1002/sd.2773
  27. Kunkel, S., Schmelzle, F., Niehoff, S., & Beier, G. (2023). More sustainable artificial intelligence systems through stakeholder involvement? GAIA - Ecological Perspectives for Science and Society, 32(S1), 64–70. https://doi.org/10.14512/gaia.32.S1.10
  28. Lewerenz, M., Passerini, E., Weber, L., Fischer, M., Olalla, N. J., Gisler, R., Emboras, A., Luisier, M., Csontos, M., Koch, U., & Leuthold, J. (2024). A Three-terminal Memristive Artificial Neuron with Tunable Firing Probability. Advanced Electronic Materials, 10(12) 2400432. https://doi.org/10.1002/aelm.202400432
  29. Linaza, M. T., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., Pagani, A., Olaizola, I. G., Barriguinha, A., Moysiadis, T., & Lucat, L. (2021). Data-driven artificial intelligence applications for sustainable precision agriculture. Agronomy, 11(6). https://doi.org/10.3390/agronomy11061227
  30. Manzoor, B., Othman, I., Durdyev, S., Ismail, S., & Wahab, M. H. (2021). Influence of artificial intelligence in civil engineering toward sustainable development—A systematic literature review. Applied System Innovation, 4(3), Article 52. https://doi.org/10.3390/asi4030052
  31. Martínez-García, A. N. (2022). Artificial intelligence for sustainable complex socio-technical-economic ecosystems. Computation, 10(6), Article 95. https://doi.org/10.3390/computation10060095
  32. Martini, B., Bellisario, D., & Coletti, P. (2024). Human-Centered and Sustainable Artificial Intelligence in Industry 5.0: Challenges and perspectives. Sustainability, 16(13). https://doi.org/10.3390/su16135448
  33. Marvin, H. J. P., Bouzembrak, Y., Fels-Klerx, H. J. van der, Kempenaar, C., Veerkamp, R., Chauhan, A., Stroosnijder, S., Top, J., Simsek-Senel, G., Vrolijk, H., Knibbe, W. J., Zhang, L., Boom, R., & Tekinerdogan, B. (2022). Digitalisation and artificial intelligence for sustainable food systems. Trends in Food Science and Technology, 120, 344–348. https://doi.org/10.1016/j.tifs.2022.01.020
  34. Mushtaq, M. A., Ahmed, H. G. M. D., & Zeng, Y. (2024). Applications of artificial intelligence in wheat breeding for sustainable food security. Sustainability, 16(13), Article 5688. https://doi.org/10.3390/su16135688
  35. Nasir, O., Javed, R. T., Gupta, S., Vinuesa, R., & Qadir, J. (2023). Artificial intelligence and sustainable development goals nexus via four vantage points. Technology in Society, 72. https://doi.org/10.1016/j.techsoc.2022.102171
  36. Nie, J., Wang, Y., Li, Y., & Chao, X. (2022). Artificial intelligence and digital twins in sustainable agriculture and forestry: A survey. Turkish Journal of Agriculture and Forestry, 46(5), 642–661. https://doi.org/10.55730/1300-011X.3033
  37. Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53. https://doi.org/10.1016/j.ijinfomgt.2020.102104
  38. Ofek, N., & Maimon, O. (2023). Beyond metrics: Navigating AI through Sustainable Paradigms. Sustainability, 15(24). https://doi.org/10.3390/su152416789
  39. Pal, P., Li, H., Al-Ajeil, R., Mohammed, A. K., Rezk, A., Melinte, G., Nayfeh, A., Shetty, D., & El-Atab, N. (2024). Energy Efficient Memristor Based on Green-Synthesized 2D Carbonyl-Decorated Organic Polymer and Application in Image Denoising and Edge Detection: Toward Sustainable AI. Advanced Science, 11(45), 2408648. https://doi.org/10.1002/advs.202408648
  40. Palmini, O., & Cugurullo, F. (2024). Design culture for sustainable urban artificial intelligence: Bruno Latour and the search for a different AI urbanism. Ethics and Information Technology, 26(1). https://doi.org/10.1007/s10676-024-09752-y
  41. Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F. J., Molina, D., Fernández, B., Moral, C., Marchena, R., Vargas, J. P. de, & Herrera, F. (2021). A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Applied Intelligence, 51(9), 6497–6527. https://doi.org/10.1007/s10489-021-02264-y
  42. Paneru, B., Paneru, B., Sapkota, S. C., & Poudyal, R. (2024). Enhancing healthcare with AI: Sustainable AI and IoT-Powered ecosystem for patient aid and interpretability analysis using SHAP. Measurement: Sensors, 36. https://doi.org/10.1016/j.measen.2024.101305
  43. Perucica, N., & Andjelkovic, K. (2022). Is the future of AI sustainable? A case study of the European Union. Transforming Government: People, Process and Policy, 16(3), 347–358.
  44. Raman, R., Pattnaik, D., Lathabai, H. H., Kumar, C., Govindan, K., & Nedungadi, P. (2024). Green and sustainable AI research: An integrated thematic and topic modeling analysis. Journal of Big Data, 11(1). https://doi.org/10.1186/s40537-024-00920-x
  45. Robbins, S., & Wynsberghe, A. van. (2022). Our New Artificial Intelligence Infrastructure: Becoming Locked into an Unsustainable Future. Sustainability, 14(8). https://doi.org/10.3390/su14084829
  46. Rohde, F., Wagner, J., Meyer, A., Reinhard, P., Voss, M., Petschow, U., & Mollen, A. (2024). Broadening the perspective for sustainable artificial intelligence: Sustainability criteria and indicators for artificial intelligence systems. Current Opinion in Environmental Sustainability, 66, Article 101411. https://doi.org/10.1016/j.cosust.2023.101411
  47. Sætra, H. S. (2021). Ai in context and the sustainable development goals: Factoring in the unsustainability of the sociotechnical system. Sustainability, 13(4), 1–19. https://doi.org/10.3390/su13041738
  48. Saheb, T., Dehghani, M., & Saheb, T. (2022). Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis. Sustainable Computing: Informatics and Systems, 35. https://doi.org/10.1016/j.suscom.2022.100699
  49. Schoormann, T., Strobel, G., Petrik, D., Möller, F., & Zschech, P. (2023). Artificial intelligence for sustainability—A systematic review of information systems literature. Communications of the Association for Information Systems, 52, 556–592. https://doi.org/10.17705/1cais.05209
  50. Schütze, P. (2024). The impacts of AI futurism: An unfiltered look at AI’s true effects on the climate crisis. Ethics and Information Technology, 26(2). https://doi.org/10.1007/s10676-024-09758-6
  51. Sen, S., Gonzalez, V., Husom, E. J., Tverdal, S., Tokas, S., & Tjøsvoll, S. O. (2024). ERG-AI: enhancing occupational ergonomics with uncertainty-aware ML and LLM feedback. Applied Intelligence. https://doi.org/10.1007/s10489-024-05796-1
  52. Seo, C., Yoo, D., & Lee, Y. (2024). Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization. Sustainability, 16(12). https://doi.org/10.3390/su16125095
  53. Shang, Y., Yang, Q., Pu, Y., & Taghizadeh-Hesary, F. (2024). Employing artificial intelligence and enhancing resource efficiency to achieve carbon neutrality. Resources Policy, 88. https://doi.org/10.1016/j.resourpol.2023.104510
  54. Sharma, A., Podoplelova, E., Shapovalov, G., Tselykh, A., & Tselykh, A. (2021). Sustainable smart cities: Convergence of artificial intelligence and blockchain. Sustainability, 13(23). https://doi.org/10.3390/su132313076
  55. Sharma, S., Gahlawat, V. K., Rahul, K., Mor, R. S., & Malik, M. (2021). Sustainable innovations in the food industry through artificial intelligence and big data analytics. Logistics, 5(4), Article 66. https://doi.org/10.3390/logistics5040066
  56. Sipola, J., Saunila, M., & Ukko, J. (2023). Adopting artificial intelligence in sustainable business. Journal of Cleaner Production, 426. https://doi.org/10.1016/j.jclepro.2023.139197
  57. Steingard, D., Balduccini, M., & Sinha, A. (2023). Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs). AI and Society, 38(2), 613–629. https://doi.org/10.1007/s00146-022-01459-2
  58. Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2024). AI-driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921–1947. https://doi.org/10.1002/sd.3221
  59. Tabbakh, A., Amin, L. A., Islam, M., Mahmud, G. M. I., Chowdhury, I. K., & Mukta, M. S. H. (2024). Towards sustainable AI: a comprehensive framework for Green AI. Discover Sustainability, 5(1). https://doi.org/10.1007/s43621-024-00641-4
  60. Tandale, S. B., & Stoffel, M. (2023). Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics. Computer Methods in Applied Mechanics and Engineering, 412. https://doi.org/10.1016/j.cma.2023.116095
  61. Truby, J. (2020). Governing Artificial Intelligence to benefit the UN Sustainable Development Goals. Sustainable Development, 28(4), 946–959. https://doi.org/10.1002/sd.2048
  62. Ueda, D., Walston, S. L., Fujita, S., Fushimi, Y., Tsuboyama, T., Kamagata, K., Yamada, A., Yanagawa, M., Ito, R., Fujima, N., Kawamura, M., Nakaura, T., Matsui, Y., Tatsugami, F., Fujioka, T., Nozaki, T., Hirata, K., & Naganawa, S. (2024). Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future. Diagnostic and Interventional Imaging, 105(11). https://doi.org/10.1016/j.diii.2024.06.002
  63. Vaio, A. D., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283–314. https://doi.org/10.1016/j.jbusres.2020.08.019
  64. Vogginger, B., Rostami, A., Jain, V., Arfa, S., Hantsch, A., Kappel, D., Schäfer, M., Faltings, U., Gonzalez, H. A., Liu, C., Mayr, C. (2024). Neuromorphic hardware for sustainable AI data centers. ArXiv, abs/2402.02521. https://doi.org/10.48550/arXiv.2402.02521
  65. Walk, J., Kühl, N., Saidani, M., & Schatte, J. (2023). Artificial intelligence for sustainability: Facilitating sustainable smart product-service systems with computer vision. Journal of Cleaner Production, 402. https://doi.org/10.1016/j.jclepro.2023.136748
  66. van Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics, 1(3), 213–218. https://doi.org/10.1007/s43681-021-00043-6
  67. Wang, Q., Zhang, F., & Li, R. (2025). Artificial intelligence and sustainable development during urbanization: Perspectives on AI R&D innovation, AI infrastructure, and AI market advantage. Sustainable Development, 33(1), 1136–1156. https://doi.org/10.1002/sd.3150
  68. Wang, Q., Li, Y., & Li, R. (2024b). Ecological footprints, carbon emissions, and energy transitions: The impact of artificial intelligence (AI). Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03520-5
  69. Wilson, C., & Velden, M. van der. (2022). Sustainable AI: An integrated model to guide public sector decision-making. Technology in Society, 68. https://doi.org/10.1016/j.techsoc.2022.101926
  70. Wu, J., & Shang, S. (2020). Managing uncertainty in ai-enabled decision making and achieving sustainability. Sustainability, 12(21), 1–17. https://doi.org/10.3390/su12218758
  71. Yaghoubi, V., & Kumru, B. (2024). Retrosynthetic Life Cycle Assessment: A Short Perspective on the Sustainability of Integrating Thermoplastics and Artificial Intelligence Into Composite Systems. Advanced Sustainable Systems, 8(5). https://doi.org/10.1002/adsu.202300543
  72. Yang, S. J. H., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100008
  73. Yigitcanlar, T., & Cugurullo, F. (2020). The sustainability of artificial intelligence: An urbanistic viewpoint from the lens of smart and sustainable cities. Sustainability, 12(20), 1–24. https://doi.org/10.3390/su12208548
  74. Yigitcanlar, T., Mehmood, R., & Corchado, J. M. (2021). Green artificial intelligence: Towards an efficient, sustainable and equitable technology for smart cities and futures. Sustainability, 13(16). https://doi.org/10.3390/su13168952
  75. Yun, J. H. J., Lee, D., Ahn, H., Park, K., & Yigitcanlar, T. (2016). Not deep learning but autonomous learning of open innovation for sustainable artificial intelligence. Sustainability, 8(8). https://doi.org/10.3390/su8080797
  76. Zhao, J. (2024). Promoting more accountable AI in the boardroom through smart regulation. Computer Law and Security Review, 52. https://doi.org/10.1016/j.clsr.2024.105939
  77. Zhao, J., & Fariñas, B. G. (2023). Artificial Intelligence and Sustainable Decisions. European Business Organization Law Review, 24(1), 1–39. https://doi.org/10.1007/s40804-022-00262-2