The Sustainability of Artificial Intelligence: Responsible Artificial Intelligence

Authors

DOI:

https://doi.org/10.5281/zenodo.12637303

Keywords:

artificial intelligence, sustainability, responsible artificial intelligence, explainability

Abstract

The importance of artificial intelligence is increasing day by day as being the first rank disruptive technologies of digital transformation. While people are trying to benefit from using artificial intelligence, they are also afraid of losing their jobs. While it is being watched with concern that ethical experiments are being conducted on who autonomous vehicles should hit in the event of an accident, it is also promising that great progress has been made in disease detection through artificial intelligence. While it is being discussed that artificial intelligence can hallucinate, studies are emerging about it’s biases which make discriminatory decisions on issues such as gender, race, etc. All these developments, when considered in the context of the social dimension of sustainability, which has been on our agenda as another important issue in recent years, show that studies should be carried out on the human and society side. Beyond contributing to economic, environmental and social sustainability with artificial intelligence, the concept of responsible artificial intelligence should be discussed in order for artificial intelligence itself to be sustainable. At this point, issues such as ethics and explainability come to the fore, it should not be overlooked that the concept of responsibility is a concept that includes these concepts but goes beyond them. This study was designed to provide general information on responsible artificial intelligence, which has been included in international literature for the last few years, but has been identified as a gap in Turkish literature. In this context, as a result of the literature review conducted on responsible artificial intelligence, which is a relatively limited and new field of study, it is revealed that studies have been carried out in the last few years and it needs to be supported by interdisciplinary and cross-cultural studies.

References

Ahmed, I., Jeon, G., ve Piccialli, F. (2022). From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where. Transactions on Industrial Informatics, 18(8).

Arrieta, A. B., Díaz-Rodríguez, N., Ser, J. D., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., ve Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI. Information Fusion, 58, 82-115.

Atiker, E. Ş. (2024) Güzel Sanatlar Ekseninde Sorumlu Yapay Zekâ: Potansiyel Riskler ve Etik Boyutlar, Reflektif Journal of Social Sciences, 5(1) DOI: 10.47613/reflektif.2024.149

Avundukluoğlu, P. (2023) SAI20 2023 Gündemi: Mavi Ekonomi Ve Sorumlu Yapay Zekâ, Sayıştay Dergisi, 34 (128)

Brundtland, G.H. 1987. “Report of the world commission on environment and development: Our common Future”

Canbay, P. ve Demircioğlu, Z. (2021). Endüstri 5.0’a Doğru: Zeki Otonom Sistemlerde Etik ve Ahlaki Sorumluluklar. Academic Journal of Information Technology, 12 (45).

Carabantes, M. (2020). Black-box Artificial Intelligence: An Epistemological and Critical Analysis. AI & Society, 35, 309–317.

Coeckelbergh, M. (2022) Foreword, İçinde D.O. Eke · K. Wakunuma & S. Akintoye (Editörler) Responsible AI in Africa Challenges and Opportunities(s. v-x). Switzerland: Plagrave McMillan

DARPA (Defense Advanced Research Projects Agency) (2016). Explainable Artificial Intelligence (XAI). DARPA-BAA-16-53. https://www.darpa.mil/attachments/DARPA-BAA-16-53.pdf, Erişim Tarihi: 24.08.2022.

Deniz, N. (2022). Lojistikte Açıklanabilir Yapay Zekâ. İçinde E. Gelmez (Editör), Lojistikte Güncel Yaklaşımlar, Ankara: Nobel.

Deniz, N. Ve Büyük, K. (2023). İkiz Dönüşüm: Sürdürülebilir ve Dijital Dönüşüm, Dijital Teknolojiler ve Eğitim Dergisi, 2(1) 57-70.

Deliloğlu, S., & Çakmak Pehlivanlı, A. (2021). Hibrit Açıklanabilir Yapay Zekâ Tasarımı ve LIME Uygulaması. Avrupa Bilim ve Teknoloji Dergisi, 27, 228-236.

Dignum, V. (2017). Responsible Artifıcial Intelligence: Designing AI For Human Values, ITU Journal: ICT Discoveries, 1.

Dignum, V. (2020). Responsibility and Artificial Intelligence, İçinde M. D. Dubber F.Pasquale & S. Das (Editörler), The Oxford Handbook of Ethics of AI (s 217-219). Oxford: Oxford University Press

Dignum, V. (2022). Responsible Artificial Intelligence: Recommendations and Lessons Learned İçinde D.O. Eke · K. Wakunuma & S. Akintoye (Editörler) Responsible AI in Africa Challenges and Opportunities(s. 195-215). Switzerland: Plagrave McMillan

Gupta, S. Kamboj S. Ve Bag S. (2023). Role of Risks in the Development of Responsible Artificial Intelligence in the Digital Healthcare Domain, Information Systems Frontiers, 25:2257–2274

Bozkurt Gümrükçüoğlu Y., Yakacak G. A. (2023). Yapay zekânın işe alım süreçlerinde kullanımı ve algoritmik ayrımcılık, Ankara Üni. Hukuk Fak. Dergisi, 72 (4),1701-1757.

Herrmann H. (2023). What’s next for responsible artificial intelligence: a way forward through responsible innovation Heliyon, 9, e14379

IBM, https://www.ibm.com/tr-tr/watson/explainable-ai, Erişim Tarihi: 19.07.2022.

Meske, C., Bunde, E., Schneider, J., ve Gersch, M. (2022). Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities. Information Systems Management, 39(1), 53-63.

Mugurusi, G. & Oluka, P. N. (2021). Towards Explainable Artificial Intelligence (XAI) in Supply Chain Management: A Typology and Research Agenda. IFIP Advances in Information and Communication Technology, 633.

Samek, W., Wiegand, T., ve Müller, K.R. (2017). Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. http://arxiv.org/abs/1708.08296v1.

Sio, F. S.· Mecacci, G. (2021). Four Responsibility Gaps with Artifcial Intelligence: Why they Matter and How to Address them Philosophy & Technology, 34:1057–1084

Elkington, J. (1994). “Towards the sustainable corporation: win-win-win business strategies for sustainable development”. California Management Review. 36(2), 90-100.

Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E. vd. (2020). “Bias in data-driven artificial intelligence systems- An introductory survey”, WIREs Data Mining Knowledge and Discovery, 10, 1356.

Ulusal Yapay Zekâ Stratejisi, https://cbddo.gov.tr/SharedFolderServer/Genel/File/TR-UlusalYZStratejisi2021-2025.pdf Son Erişim Tarihi: 7 Aralık 2023.

Wang, Y., Xiong, M. ve Olya, H. (2020) Toward an understanding of responsible artificial intelligence practices. In: Bui, T.X., (ed.) Proceedings of the 53rd Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences (HICSS 2020), 07-10 Jan 2020, Maui, Hawaii, USA. Hawaii International Conference on System Sciences (HICSS) , s. 4962-4971.

Published

2024-06-30

How to Cite

Deniz, N. (2024). The Sustainability of Artificial Intelligence: Responsible Artificial Intelligence. Journal of Digital Technologies and Education, 3(1), 69–79. https://doi.org/10.5281/zenodo.12637303
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