Yapay Zekâ Entegrasyonu ve Sosyal İnovasyon: Sürdürülebilir Kalkınma Amaçlarıyla Uyumlu Disiplinlerarası Araştırma Eğilimleri

Yıl 2024, Cilt: 5 Sayı: 3, 418 - 443, 28.10.2024

Ayşe Aslı Yılmaz

https://doi.org/10.54733/smar.1543390

Öz

Bu çalışma, Yapay Zeka (YZ), Makine Öğrenimi, Doğal Dil İşleme (NLP) ve Prompt Mühendisliği'nin sosyal bilimlere entegrasyonunu ve bu teknolojilerin işbirlikçi ağlar, tematik gelişmeler ve Sürdürülebilir Kalkınma Amaçları (SKA'lar) ile uyumlu araştırma eğilimleri üzerindeki etkilerini incelemektedir. Bibliyometrik analiz ve konu modelleme yöntemlerini kullanan araştırma, son on yılı kapsayan Web of Science (WoS) veri tabanından elde edilen 389 yayını analiz etmektedir. Bulgular, bu teknolojilerin sosyal bilimlerle kesişiminde disiplinlerarası araştırmalarda önemli bir büyüme olduğunu ve özellikle yönetim, işletme ve çevre çalışmaları alanlarında kayda değer katkılar yapıldığını ortaya koymaktadır. Çalışma, YZ'nin ürün geliştirmede yenilikçi uygulamaları, enerji sektöründeki ilerlemeler ve eğitim ile sağlık alanlarındaki kullanımı gibi ana temaları belirlemektedir. Araştırma, YZ'nin sürdürülebilir kalkınmayı desteklemedeki dönüştürücü potansiyeline vurgu yaparken, etik kaygıların ele alınmasının ve sorumlu bir şekilde uygulanmasının önemine de dikkat çekmektedir. Bu çalışma, YZ ve ilgili teknolojilerin sosyal bilimleri nasıl yeniden şekillendirdiği ve küresel sürdürülebilirlik hedeflerine ulaşmada oynadığı rol hakkında daha derin bir anlayışa katkı sağlamaktadır.

Anahtar Kelimeler

yapay zeka entegrasyonu, sürdürülebilir kalkınma amaçları, sosyal inovasyon

Artificial Intelligence Integration and Social Innovation: Interdisciplinary Research Trends Aligned with the Sustainable Development Goals

Öz

This study investigates the integration of Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), and Prompt Engineering into the social sciences and their impact on collaborative networks, thematic developments, and research trends aligned with the Sustainable Development Goals (SDGs). Utilizing bibliometric analysis and topic modeling, the research analyzes a dataset of 389 publications from the Web of Science (WoS) database, spanning the last decade. The findings highlight significant growth in interdisciplinary research at the intersection of these technologies and social sciences, with notable contributions in management, business, and environmental studies. The study identifies key themes such as AI-driven innovation in product development, progress in the energy sector, and the use of AI in educational and healthcare environments. It highlights AI’s transformative potential in promoting sustainable development, while also stressing the significance of addressing ethical concerns and ensuring responsible application. This research contributes to a deeper understanding of how AI and related technologies are reshaping the social sciences and their role in achieving global sustainability goals.

Anahtar Kelimeler

artificial intelligence integration, sustainable development goals, social innovation

Kaynakça

• Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., ... & Martinis, J. M. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505-510. https://doi.org/10.1038/s41586-019-1666-5
• Batarseh, F. A., Freeman, L., & Huang, C. H. (2021). A survey on artificial intelligence assurance. Journal of Big Data, 8, 60. https://doi.org/10.1186/s40537-021-00445-7
• Bessen, J. (2019). AI and jobs: The role of demand. NBER Working Paper Series, Working Paper 24235.
• Brynjolfsson, E., & McAfee, A. (2017). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
• Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), 1-38. https://doi.org/10.1145/3502289
• Černevičienė, J., & Kabasinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57, 216. https://doi.org/10.1007/s10462-024-10854-8
• Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
• Ditlev-Simonsen, C. D. (2022). The business case for sustainability. In C. D. Ditlev-Simonsen (Ed.), A guide to sustainable corporate responsibility from theory to action (pp. 103-128). Springer. https://doi.org/10.1007/978-3-030-88203-7_5
• Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Effy, V. (2021). An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. In L. Floridi (Ed.), Ethics, governance, and policies in artificial intelligence (pp. 19-39). Springer. https://doi.org/10.1007/978-3-030-81907-1_3
• Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: Strategy and leadership when algorithms and networks run the world. Harvard Business Review Press.
• Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31, 685-695. https://doi.org/10.1007/s12525-021-00475-2
• Li, Q., Zhu, J., & Xiao, Q. (2024). Accurate building energy management based on artificial intelligence. Applied Mathematics and Nonlinear Sciences, 9(1), 1-19. https://doi.org/10.2478/amns-2024-1359
• Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Del Ser, J., ... & Stumpf, S. (2024). Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. Information Fusion, 106, 102301. https://doi.org/10.1016/j.inffus.2024.102301
• Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91-90. https://doi.org/10.1016/j.segan.2016.02.005
• Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press. https://doi.org/10.2307/j.ctt1pwt9w5
• Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
• Padmaja C. V. R., Narayana, S. L., Anga, G. L., & Bhansali, P. K. (2024). The rise of AI: A comprehensive research review. IAES International Journal of Artificial Intelligence (IJ-AI), 13(2), 2226-2235. https://doi.org/10.11591/ijai.v13.i2.pp2226-2235
• Regona, M., Yigitcanlar, T., Hon, C., & Teo, M. (2024). Artificial intelligence and sustainable development goals: Systematic literature review of the construction industry. Sustainable Cities and Society, 108, 105499. https://doi.org/10.1016/j.scs.2024.105499
• Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., Lambin, E. F., ... & Foley, J. A. (2009). A safe operating space for humanity. Nature, 461, 472-475. https://doi.org/10.1038/461472a
• Tan, P., Chen, X., Zhang, H., Wei, Q., & Luo, K. (2023). Artificial intelligence aids in development of nanomedicines for cancer management. Seminars in Cancer Biology, 89, 61-75. https://doi.org/10.1016/j.semcancer.2023.01.005
• Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11, 233. https://doi.org/10.1038/s41467-019-14108-y
• Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., ... & Silver, D. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575, 350-354. https://doi.org/10.1038/s41586-019-1724-z
• Weber, P. (2023). Unrealistic optimism regarding artificial intelligence opportunities in human resource management. International Journal of Knowledge Management, 19(1), 1-19. https://doi.org/10.4018/IJKM.317217
• Xie, Y., Xia, Y., Zhang, J., Song, Y., Feng, D., Fulham, M., & Cai, W. (2018). Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Transactions on Medical Imaging, 38(4), 991-1004. https://doi.org/10.1109/TMI.2018.2876510