Journals / Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Cilt: 35

ARTIFICIAL INTELLIGENCE AND ETHICS: A GLOBAL PERSPECTIVE

YAPAY ZEKA VE ETİK: KÜRESEL BİR BAKIŞ

Elif Simge Güzelergene(PAMUKKALE ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ)0000-0001-6629-6543
Deniz Baransel Cinar(PAMUKKALE ÜNİVERSİTESİ)0000-0003-1614-1214
Funda Nayır(AĞRI İBRAHİM ÇEÇEN ÜNİVERSİTESİ)0000-0002-9313-4942
Pages
—
Publication Date
2026-04-28
🏛️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)

However, the findings reveal a fundamental pattern: while organizations demonstrate normative convergence around shared ethical values, they exhibit operational divergence in how these principles are framed, justified, and institutionalized within distinct governance contexts.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The rapid development and widespread adoption of artificial intelligence technologies necessitate their alignment with ethical principles throughout design and deployment processes.
📊
Key Findings & Data:However, the findings reveal a fundamental pattern: while organizations demonstrate normative convergence around shared ethical values, they exhibit operational divergence in how these principles are framed, justified, and institutionalized within distinct governance contexts.
💡
Conclusion & Impact:By analytically unpacking this implementation gap, the research advances AI ethics scholarship beyond principle identification and provides actionable insights for policymakers and practitioners.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zeka Etiği · Etik uygulama boşluğu · Yönetişim yaklaşımları

Abstract

The rapid development and widespread adoption of artificial intelligence technologies necessitate their alignment with ethical principles throughout design and deployment processes. This study comparatively examines five major international reports on AI ethics published by UNESCO, the European Commission, the OECD, the European Parliament, and the Council of Europe. Employing document analysis and thematic coding methods, the research identifies transparency, responsibility, and trustworthiness as core ethical principles commonly emphasized across these frameworks. However, the findings reveal a fundamental pattern: while organizations demonstrate normative convergence around shared ethical values, they exhibit operational divergence in how these principles are framed, justified, and institutionalized within distinct governance contexts. This produces an ethical implementation gap, the distance between widely endorsed norms and their practical realization in policy frameworks. UNESCO pursues culturally adaptive norms through education and capacity building; the European Commission and European Parliament enforce compliance through binding regulations; OECD coordinates flexible implementation across member states; and the Council of Europe anchors AI ethics within human rights law. These divergent governance logics reflect deeper philosophical disagreements about whether ethical AI should be realized through voluntary norms, technical standards, binding legislation, or rights-based frameworks. The study demonstrates that contemporary challenges in AI ethics stem not from a lack of shared principles but from incompatible mechanisms for their operationalization. By analytically unpacking this implementation gap, the research advances AI ethics scholarship beyond principle identification and provides actionable insights for policymakers and practitioners.

Özet

Yapay zekâ teknolojilerinin hızla gelişmesi ve yaygınlaşması, bu teknolojilerin etik ilkelerle uyumlu biçimde geliştirilmesini zorunlu kılmaktadır. Bu çalışma, UNESCO, Avrupa Komisyonu, OECD, Avrupa Parlamentosu ve Avrupa Konseyi tarafından yayımlanan beş büyük uluslararası yapay zekâ etiği raporunu karşılaştırmalı olarak incelemektedir. Doküman analizi ve tematik kodlama yöntemiyle yürütülen araştırmada, şeffaflık, sorumluluk ve güvenilirlik ilkelerinin tüm raporlarda ortak biçimde vurgulandığı tespit edilmiştir. Bulgular, kuruluşların ortak etik değerlerde uzlaşırken, bu ilkelerin uygulanmasında önemli farklılıklar gösterdiğini ortaya koymaktadır. UNESCO eğitim ve kapasite geliştirmeyle kültürel uyarlama; Avrupa Komisyonu ve Parlamentosu bağlayıcı yasal düzenlemeler; OECD esnek işbirliği; Avrupa Konseyi ise insan hakları hukuku yoluyla etik uygulamayı hayata geçirmektedir. Bu farklılaşma, çalışmanın temel kavramı olan “etik uygulama boşluğunu”, başka bir ifade ile “yaygın kabul gören ilkeler ile gerçek uygulamalar arasındaki açığı” doğurmaktadır. Çalışma, yapay zekâ etiğindeki güncel zorlukların paylaşılan ilkelerin eksikliğinden değil, bu ilkelerin hayata geçirilmesine yönelik uyumsuz mekanizmalardan kaynaklandığını göstermektedir. Bu uygulama boşluğunu analitik olarak açıklayarak, araştırma yapay zekâ etiği literatürünü ilke belirlemenin ötesine taşımakta ve politika yapıcılar ile uygulayıcılar için eylem odaklı öneriler sunmaktadır.

🔖

Cite This Article

Güzelergene, E. S., Cinar, D. B., Nayır, F. (2026). ARTIFICIAL INTELLIGENCE AND ETHICS: A GLOBAL PERSPECTIVE. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 35. https://doi.org/10.35379/cusosbil.1685334

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (50)

  1. Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244. https://doi.org/10.1086/705716
  2. Allen, C., Wallach, W., & Smit, I. (2006). Why machine ethics? IEEE Intelligent Systems, 21(4), 12-17. https://doi.org/10.1109/MIS.2006.83
  3. Anderson, M., & Anderson, S. L. (2007). Machine ethics: Creating an ethical intelligent agent. AI Magazine, 28(4), 15-26. https://doi.org/10.1609/aimag.v28i4.2065
  4. Batool, A., Lee, S., Liu, Y., & Dong, L. (2026). The anatomy of AI policies: a systematic comparative analysis of AI policies across the globe. AI and Ethics 6, 55. https://doi.org/10.1007/s43681-025-00886-3
  5. Batool, A., Zowghi, D. & Bano, M. (2025). AI governance: a systematic literature review. AI and Ethics 5, 3265-3279. https://doi.org/10.1007/s43681-024-00653-w
  6. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the Conference on Fairness, Accountability and Transparency (FAT*) (pp. 149–159). PMLR.
  7. Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40. https://doi.org/10.3316/QRJ0902027
  8. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
  9. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
  10. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the Conference on Fairness, Accountability and Transparency (FAT*) (pp. 77–91). PMLR.
  11. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
  12. Corrêa, N. K., Galvão, C., Santos, J. W., Del Pino, C., Pinto, E. P., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., Terem, E., & de Oliveira, N. (2023). Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance. Patterns, 4(10), 100857. https://doi.org/10.1016/j.patter.2023.100857
  13. Corrêa, N. K., Santos, J. W., Galvão, C., Pasetti, M., Schiavon, D., Naqvi, F., Hossain, R., & de Oliveira, N. (2025). Crossing the principle-practice gap in AI ethics with ethical problem-solving. AI and Ethics 5, 1271-1288. https://doi.org/10.1007/s43681-024-00469-8
  14. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. https://doi.org/10.12987/9780300252392
  15. Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56-62. https://doi.org/10.1145/2844110
  16. Ding, J. (2018). Deciphering China’s AI dream. Future of Humanity Institute, University of Oxford. https://cdn.governance.ai/Deciphering_Chinas_AI-Dream.pdf
  17. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://doi.org/10.48550/arXiv.1702.08608
  18. Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021). Deep learning-enabled medical computer vision. NPJ Digital Medicine, 4(1), 5. https://doi.org/10.1038/s41746-020-00376-2
  19. European Commission (2019). Ethics Guidelines for Trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
  20. European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Text with EEA relevance). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
  21. Fox-Skelly, J., Bird, E., & Jenner, N. (2020). The ethics of artificial intelligence: Issues and initiatives. European Parliament, Directorate-General for Parliamentary Research Services. https://data.europa.eu/doi/10.2861/6644
  22. European Parliamentary Research Service. (2024). AI investment: EU and global indicators. European Parliament. https://www.europarl.europa.eu/RegData/etudes/ATAG/2024/760392/EPRS_ATA(2024)760392_EN.pdf
  23. European Parliamentary Research Service. (2023). China-US global rivalry and the EU (Briefing 749803). European Parliament. https://www.europarl.europa.eu/RegData/etudes/BRIE/2023/749803/EPRS_BRI(2023)749803_EN.pdf
  24. Floridi, L. (2014). The fourth revolution: How the infosphere is reshaping human reality. Oxford University Press.
  25. Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. In S. Carta (Ed.), Machine Learning and the City: Applications in Architecture and Urban Design (pp. 535-545). Wiley. https://doi.org/10.1002/9781119815075.ch45
  26. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
  27. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  28. Israel, I. B., Cerdio, J., Ema, A., Friedman, L., Ienca, M., Mantelero, A., & Matania, E. (2020). Towards regulation of AI systems: Global perspectives on the development of a legal framework on artificial intelligence systems based on the Council of Europe’s standards on human rights, democracy and the rule of law. Council of Europe.
  29. Lipton, Z. C. (2018). The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31-57. https://doi.org/10.1145/3236386.3241340
  30. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.
  31. McKinsey Global Institute. (2023). The economic impact of artificial intelligence. McKinsey & Company. https://www.mckinsey.com
  32. Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.
  33. Milakis, D., Van Arem, B., & Van Wee, B. (2017). Policy and society related implications of automated driving: A review of literature and directions for future research. Journal of Intelligent Transportation Systems, 21(4), 324-348. https://doi.org/10.1080/15472450.2017.1291351
  34. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679
  35. OECD. (2019). Recommendation of the council on artificial intelligence. OECD.
  36. OECD. (2023). The state of implementation of the OECD AI Principles four years on. https://www.oecd.org/en/publications/the-state-of-implementation-of-the-oecd-ai-principles-four-years-on_835641c9-en.html
  37. O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
  38. Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. Information Systems Frontiers, 25(1), 123–141. https://doi.org/10.1007/s10796-022-10251-y
  39. Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885
  40. PricewaterhouseCoopers (PwC). (2017). Sizing the prize: What’s the real value of AI for your business and how can you capitalise? https://www.pwc.com.au/government/pwc-ai-analysis-sizing-the-prize-report.pdf Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
  41. Sharma, S. (2024). Benefits or concerns of AI: A multistakeholder responsibility. Futures, 157, 103328. https://doi.org/10.1016/j.futures.2024.103328
  42. Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: Ethics of AI and ethical AI. Journal of Database Management, 31(2), 74-87. https://doi.org/10.4018/JDM.2020040105
  43. Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751-752. https://doi.org/10.1126/science.aat5991
  44. UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000381137
  45. Van Otterlo, M. (2017). From algorithmic black boxes to adaptive white boxes: Declarative decision-theoretic ethical programs as codes of ethics. arXiv. https://doi.org/10.48550/arXiv.1711.06035
  46. Whittlestone, J., Nyrup, R., Alexandrova, A., & Cave, S. (2019, January). The role and limits of principles in AI ethics: Towards a focus on tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 195-200).
  47. Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 1-18. https://doi.org/10.1145/3351095.3372833
  48. Xiong, H., Ledwidge, M. T., Fadahunsi, K. P., Lee, H. Y., Wu, J., Morrow, S., Nisar, Y. B., Mbakaya, B., O’Donoghue, J., &
  49. Gallagher, J. (2025). Global Artificial Intelligence (AI) Governance, Trust, and Ethics for Sustainable Health (GATES): A Protocol for Methodological Framework. VeriXiv, 2, 187. https://doi.org/10.12688/verixiv.1380.1
  50. Zarsky, T. (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology, & Human Values, 41(1), 118-132. https://doi.org/10.1177/01622439156057