Trustworthy Artificial Intelligence At Work: A Conceptual Assessment of Ethical Dimensions

Yıl 2025, Cilt: 5 Sayı: 2, 54 - 72, 31.12.2025

Meltem Nilüfer Önen

Öz

Artificial intelligence (AI), one of the most debated and researched topics in recent years, is rapidly developing with initiatives aimed at increasing its existing potential, such as making individuals' lives easier, and ensuring global development and social welfare. However, this unpredictability in the rapid development and spread of AI brings with it a number of technical and ethical problems. These problems, in turn, trigger concerns about reliability and controllability in the workplace, which is one of the most frequently used areas of AI, as it is not yet possible to speak of a nationally accepted, common AI ethical awareness. Studies conducted on this matter have not yet created the expected impact and have failed to alleviate concerns about the reliability of AI, neither in terms of the future of organizations and professions nor the security of institutions and employees. In this context, this study aims to address the reliability of AI systems used in working life from an ethical perspective, and to present a conceptual framework based on business ethics by examining the ethical dimensions of reliable AI in working life within the framework of the principles of human control, privacy, security, transparency, and accountability.

Anahtar Kelimeler

Artificial Intelligence , Trustworthy Artificial Intelligence , Ethical Principles , Working Life

Çalışma Yaşamında Güvenilir Yapay Zekâ: Etik Boyutları Üzerine Kavramsal Bir Değerlendirme

Öz

Son yılların en çok tartışılan ve araştırılan konularından biri olan yapay zekâ, bireylerin hayatını kolaylaştırmak, küresel kalkınmayı ve toplumsal refahı sağlamak gibi mevcut potansiyeli arttırmaya yönelik girişimlerle hızla gelişmektedir. Fakat, yapay zekanın hızlı gelişimi ve yaygınlaşmasındaki bu öngörülemezlik birtakım teknik ve etik sorunları da beraberinde getirmektedir. Bu sorunlar ise yapay zekanın en çok kullanıldığı alanlardan biri olan iş yaşamında, güvenilir ve kontrol edilebilir olma kaygılarını tetiklemektedir; çünkü henüz ulusal düzeyde kabul görmüş, ortak bir yapay zekâ etik bilincinden söz etmek mümkün değildir. Buna dair yapılan çalışmalar, henüz beklenen etkiyi yaratamamış olup ne örgütler ve mesleklerin geleceği açısından ne de kurumlar ve çalışanların güvenliği açısından yapay zekanın güvenirliğine duyulan endişeleri azaltamamıştır. Bu bağlamda bu çalışma, iş yaşamında kullanılan yapay zekâ sistemlerinin güvenilirliğini etik bir perspektiften ele almayı ve çalışma yaşamında güvenilir yapay zekânın etik boyutlarını insan kontrolü, gizlilik, güvenlik, şeffaflık ve hesap verebilirlik ilkeleri çerçevesinde inceleyerek, iş etiği temelli kavramsal bir çerçeve sunmayı amaçlamaktadır.

Anahtar Kelimeler

Yapay Zekâ , Güvenilir Yapay Zekâ , Etik İlkeler , Çalışma Yaşamı

Etik Beyan

Bu çalışma insan veya hayvan katılımcıları içermemektedir. Tüm prosedürler bilimsel ve etik ilkelere uygun olarak gerçekleştirilmiş olup, atıfta bulunulan tüm çalışmalar uygun şekilde kaynak gösterilmiştir.

Destekleyen Kurum

Destekleyen Kurum Yoktur.

Teşekkür

Çalışmanın değerlendirme sürecinde yapıcı geri bildirimleriyle araştırmanın bilimsel derinliğinin ve açıklığının artırılmasına katkı sağlayan hakemlere ve editöryal süreci titizlikle yürüten dergi editörlerine teşekkürlerimi sunarım.

Kaynakça

• Aktaş, K. (2014). Etik-ahlâk ilişkisi ve etiğin gelişim süreci. Uluslararası Yönetim ve Sosyal Araştırmalar Dergisi, 1(2), 22-32.
• Aley, M. R., & Levine, K. J. (2021). Popular culture at work: How emerging adults’ favorite celebrity can influence future career aspirations and work ethic. Atlantic Journal of Communication, 30(4), 1-16. https://doi.org/10.1080/15456870.2021.1936527
• Ali, A. J., Falcone, T., & Azim, A. A. (1995). Work ethic in the USA and Canada. Journal of Management Development, 14(6), 26-34. https://doi.org/10.1108/02621719510086156
• Allerton, H. (1994). Working life: Getting a life. Training and Development, 48(7), 80-83.
• Altenried, M. (2020). The platform as factory: Crowdwork and the hidden labour behind artificial intelligence. Capital & Class, 44(2), 145-158. https://doi.org/10.1177/0309816819899410
• Al-Zahrani, A. M., & Alasmari, T. M. (2024). Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications. Humanities and Social Sciences Communications, 11(1), Article 912. https://doi.org/10.1057/s41599-024-03432-4.
• Baker-Brunnbauer, J. (2021). TaII Framework for Trustworthy AI Systems. Robonomics: The Journal of the Automated Economy, 2(17), 1-12.
• Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185(3), 725-740. https://doi.org/10.1007/s10551-023-05339-7
• Barney, C. E., & Elias, S. M. (2010). Flex‐time as a moderator of the job stress‐work motivation relationship: A three nation investigation. Personnel Review, 39(4), 487-502. https://doi.org/10.1108/00483481011045434
• Bilić, P. (2016). Search algorithms, hidden labour and information control. Big Data & Society, 3(1), 1-9. https://doi.org/10.1177/2053951716652159
• Blue, G., & Hogan, M. (2024). Getting democracy wrong: How lessons from biotechnology can illuminate limits of the Asilomar AI principles. Journal of Digital Social Research, 6(4), 107-117. https://doi.org/10.33621/jdsr.v6i440477
• Bostrom, A., Demuth, J. L., Wirz, C. D., Cains, M. G., Schumacher, A., Madlambayan, D., Bansal, A. S., Bearth, A., Chase, R., Crosman, K. M., Ebert-Uphoff, I., Gagne II, D. J., Guikema, S., Hofman, R., Johnson, B. B., Kumler-Bonfanti, C., Lee, J. D., Lowe, A., McGovern, A., … Williams, J. K. (2024). Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences. Risk Analysis, 44(6), 1498-1513. https://doi.org/10.1111/risa.14245
• Brendel, A. B., Mirbabaie, M., Lembcke, T.-B., & Hofeditz, L. (2021). Ethical management of artificial intelligence. Sustainability, 13(4), Article 1974. https://doi.org/10.3390/su13041974
• Claessens, B. J., Van Eerde, W., Rutte, C. G., & Roe, R. A. (2004). Planning behavior and perceived control of time at work. Journal of Organizational Behavior, 25(8), 937-950.
• Coeckelbergh, M. (2019). Artificial intelligence: Some ethical issues and regulatory challenges. Technology and Regulation, 2019, 31-34.
• Combi, C., Amico, B., Bellazzi, R., Holzinger, A., Moore, J. H., Zitnik, M., & Holmes, J. H. (2022). A manifesto on explainability for artificial intelligence in medicine. Artificial Intelligence in Medicine, 133, Article 102423. https://doi.org/10.1016/j.artmed.2022.102423 Corbo, S. A. (1997). The X-er files. Hospitals and Health Networks, 71, 58-60.
• Eisenberger, R. (1989). Blue monday: The loss of the work ethic in America. Paragon House.
• Elkins, S. L. (2007). Job satisfaction and work ethic among workers in a Japanese manufacturing company located in the United States (Doctoral dissertation). Dissertation Abstracts International: Section A. Humanities and Social Sciences, 68(10)
• European Commission. (2019). Ethics guidelines for trustworthy AI. https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html
• Felzmann, H., Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Towards transparency by design for artificial intelligence. Science and Engineering Ethics, 26(6), 3333-3361.
• Felzmann, H., Villaronga, E.F., Lutz, C., & Tamò-Larrieux, A. (2019). Transparency You can trust: Transparency requirements for artificial ıntelligence between legal norms and contextual concerns. Big Data & Society, 6(1), 1–14.
• Fletcher, R. R., Nakeshimana, A., & Olubeko, O. (2021). Addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health. Frontiers in artificial intelligence, 3, Article 561802. https://doi.org/10.3389/frai.2020.561802
• Formosa, P. (2021). Robot autonomy vs. human autonomy: Social robots, artificial intelligence (AI), and the nature of autonomy. Minds and Machines, 31(4), 595-616. https://doi.org/10.1007/s11023-021-09579-2
• Furman, G. C. (2004). The ethic of community. Journal of Educational Administration, 42(2), 215-235.
• Furnham, A., & Koritsas, E. (1990). The protestant work ethic and vocational preference. Journal of Organizational Behavior, 11(1), 43-55. https://doi.org/10.1002/job.4030110107
• Future of Life Institute (FLI). (2017). AI principles. https://futureoflife.org/open-letter/ai-principles/
• Future of Life Institute (FLI). (2023). Pause giant AI experiments: An open letter. https://futureoflife.org/open-letter/pause-giant-ai-experiments/
• Gibson, K. (2023). Ethics and business. Cambridge University Press.
• Goveas, M. S. (2011). Effect of work life balance and ethics on quality of service. International Journal of Business and Social Science, 2(23), 282-285.
• Graham, M., Alyanak, O., & Valente, J. C. (2025). Feeding the machine: The hidden labour behind AI tools and ethical implications for higher education. Frontiers in Communication, 10, Article 1614817.
• Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30, 99-120. https://doi.org/10.1007/s11023-020-09517-8
• Hamon, R., Junklewitz, H., & Sanchez, I. (2020). Robustness and explainability of artificial intelligence. Publications Office of the European Union.
• Harkness, A. M., Long, B. C., Bermbach, N., Patterson, K., Jordan, S., & Kahn, H. (2005). Talking about work stress: Discourse analysis and implications for stress interventions. Work & Stress, 19(2), 121-136. https://doi.org/10.1080/02678370500160068
• Hashmi, E., Yamin, M. M. & Yayilgan, S. Y. (2025). Securing tomorrow: A comprehensive survey on the synergy of artificial intelligence and information security. AI Ethics, 5, 1911-1929. https://doi.org/10.1007/s43681-024-00529-z.
• Heinrichs, B. (2022). Discrimination in the age of artificial intelligence. AI & Society, 37(1), 143-154. https://doi.org/10.1007/s00146-021-01192-2
• Hill, R. B., & Fouts, S. (2005). Work ethic and employment status: A study of jobseekers. Journal of STEM Teacher Education, 42(3), 48-65.
• Hoffmann, M. (2025). The Mechanisms of AI harm: Lessons learned from AI incidents. Center for Security and Emerging Technology.
• Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining And Knowledge Discovery, 9(4), 1-13. https://doi.org/10.1002/widm.1312
• Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917-926. https://doi.org/10.1002/ajim.23037.
• Huang, C., Zhang, Z., Mao, B., & Yao, X. (2022). An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence, 4(4), 799-819. https://doi.org/10.1109/TAI.2022.3194503
• Innerarity, D. (2025). Una teoría crítica de la inteligencia artificial. Galaxia Gutenberg.
• Inshakova, A. O., Deryugina, T. V., Matytsin, D. E., & Kochetkova, S. Y. (2025). Asilomar principles for the development and use of artificial intelligence in the it sector of the economy. In Legal tech and legal-AI in business: Major and promising sectors of economy (pp. 3-13). Springer Nature Switzerland.
• Javed, H., El-Sappagh, S., & Abuhmed, T. (2024). Robustness in deep learning models for medical diagnostics: Security and adversarial challenges towards robust AI applications. Artificial Intelligence Review, 58(1), Article 12.
• Jovanović, M. (2024). The Ethical implications of artificial intelligence in the workplace: Bias, discrimination, and transparency. In Actual Economy: Local Solutions for Global challenges, (pp. 188-190).
• Koulu, R. (2020). Proceduralizing control and discretion: Human oversight in artificial intelligence policy. Maastricht Journal of European and Comparative Law, 27(6), 720-735.
• Kumari, N., & Pandey, S. (2023). Application of artificial intelligence in environmental sustainability and climate change. In Visualization techniques for climate change with machine learning and artificial intelligence (pp. 293-316). Elsevier.
• Laitinen, A., & Sahlgren, O. (2021). AI systems and respect for human autonomy. Frontiers in Artificial Intelligence, 4, Article 705164.
• Lapierre, L. M., & Allen, T. D. (2012). Control at work, control at home, and planning behavior: Implications for work–family conflict. Journal of Management, 38(5), 1500-1516. https://doi.org/10.1177/0149206310385868
• Larsson, S. (2020). On the Governance of Artificial Intelligence through Ethics Guidelines. Asian Journal of Law and Society, 7(3), 437-451. https://doi.org/10.1017/als.2020.19
• Larsson, S., & Heintz, F. (2020). Transparency in artificial intelligence. Internet Policy Review, 9(2), 1-16.
• Levent, A. F. (Ed.) (2022). Eğitimde ahlak ve etik. Nobel Akademik Yayıncılık.
• Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023). Trustworthy AI: From principles to practices. ACM Computing Surveys, 55(9), 1-46. https://doi.org/10.1145/3555803 Lim, D. H., Woehr, D. J., You, Y. M., & Allen Gorman, C. (2007). The translation and development of a short form of the Korean language version of the multidimensional work ethic profile. Human Resource Development International, 10(3), 319-331. https://doi.org/10.1080/13678860701515406
• McDermid, J. A., Jia, Y., Porter, Z., & Habli, I. (2021). Artificial intelligence explainability: The technical and ethical dimensions. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2207), Article 20200363. https://doi.org/10.1098/rsta.2020.0363
• Mehra, A. D. (2020). Unifying adversarial robustness and interpretability in deep neural networks: A comprehensive framework for explainable and secure machine learning models. International Research Journal of Modernization in Engineering Technology and Science, 2(9), 1829-1838.
• Miller, M. J., Woehr, D. J., & Hudspeth, N. (2002). The meaning and measurement of work ethic: Construction and initial validation of a multidimensional inventory. Journal of Vocational Behavior, 60(3), 451-489. https://doi.org/10.1006/jvbe.2001.1838
• Neethirajan, S. (2024). Artificial intelligence and sensor innovations: Enhancing livestock welfare with a human-centric approach. Human-Centric Intelligent Systems, 4(1), 77-92. https://doi.org/10.1007/s44230-023-00050-2
• Nolte, H., Rateike, M., & Finck, M. (2025). Robustness and cybersecurity in the EU Artificial Intelligence Act. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 283-295).
• Oxford Reference (2025, December 23). Artificial intelligence. https://www.oxfordreference.com/display/10.1093/oi/authority.20110803095426960
• Pizzolato, N. (2024). The Hidden Labour of Digital Capitalism: Changes, Continuities, Critical Issues. International Review of Social History, 69(2), 299-306.
• Prunkl, C. (2024). Human autonomy at risk? An analysis of the challenges from AI. Minds and Machines, 34(3), Article 26. https://doi.org/10.1007/s11023-024-09665-1
• Rai, A., Constantinides, P., & Sarker, S. (2019). Editor’s comments Next-generation digital platforms: Toward human-AI hybrids. MIS Quarterly, 43, 1-10.
• Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137-141.
• Reich, C., & Meder, B. (2023). The heart and artificial intelligence: How can we ımprove medicine without causing harm. Current Heart Failure Reports, 20(4), 271-279. https://doi.org/10.1007/s11897-023-00606-0
• Reinhardt, K. (2023). Trust and trustworthiness in AI ethics. AI and Ethics, 3(3), 735-744.
• Robles Carrillo, M. (2020). Artificial intelligence: From ethics to law. Telecommunications Policy, 44(6), Article 101937.
• Rogozea, L. (2009). Towards ethical aspects on artificial intelligence. In Proceedings of the 8th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, 2, 507-512.
• Sacks, P. (1998). No… generation X is not OK. American Enterprise, 9, 46-48.
• Santoni de Sio, F. (2024). Artificial Intelligence and the Future of Work: Mapping the Ethical Issues. The Journal of Ethics, 28, 407-427. https://doi.org/10.1007/s10892-024-09493-6
• Sarıyıldız, A. Y. (2020). Örgütsel bağlılık ve iş etiği. Nobel Bilimsel Eserler.
• Sharma, B. R., & Rai, S. (2015). A study to develop an instrument to measure work ethic. Global Business Review, 16(2), 244-257.
• Shefer, O., Laktionov, O., Pents, V., Hlushko, A., & Kuchuk, N. (2024). Practical principles of integrating artificial intelligence into the technology of regional security predicting. Advanced Information Systems, 8(1), 86-93.
• Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, Safe & Trustworthy. International Journal of Human–Computer Interaction, 36(6), 495-504. https://doi.org/10.1080/10447318.2020.1741118
• Simion, M., & Kelp, C. (2023). Trustworthy artificial intelligence. Asian Journal of Philosophy, 2(1), Article 8.
• Slota, S. C., Fleischmann, K. R., Greenberg, S., Verma, N., Cummings, B., Li, L., & Shenefiel, C. (2023). Locating the work of artificial intelligence ethics. Journal of the Association for Information Science and Technology, 74(3), 311-322.
• Spiegler, M. (1997). Slack attack. American Demographics, 19, Article 35.
• Sufian, A., Chandra, U., Das, S., & Chettri, D. (2020). Advancements in agriculture strategies and environmental impact: A review. SSRN Electronic Journal. https://ssrn.com/abstract=3516438
• Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets 31, 447-464.
• Tocchetti, A., Corti, L., Balayn, A., Yurrita, M., Lippmann, P., Brambilla, M., & Yang, J. (2025). A.I. robustness: A human-centered perspective on technological challenges and opportunities. ACM Computing Surveys, 57(6), 1-38. https://doi.org/10.1145/366592
• Tsamados, A., Floridi, L., & Taddeo, M. (2025). Human control of AI systems: From supervision to teaming. AI and Ethics, 5(2), 1535-1548. https://doi.org/10.1007/s43681-024-00489-4
• Turing, A. (1951). Can digital computers think?. In B.J. Copeland, (ed.), The essential Turing, Oxford University Press. https://doi.org/10.1093/oso/9780198250791.003.0019
• Turyasingura, B., Ayiga, N., Byamukama, W., Kayusi, F., Tumuhimbise, M., & Tumushabe, T. (2024). Application of artificial intelligence (AI) in environment and societal trends: Challenges and opportunities. Babylonian Journal of Machine Learning, 2024, 177-182.
• Türk Dil Kurumu Başkanlığı (TDK). (2015). Etik rehber. https://tdk.gov.tr/wp-content/uploads/2015/12/etikrehber.pdf
• Türkiye Yapay Zekâ İnisiyatifi (TRAİ). (2024). Yapay zekâ etik ilkeleri ve hukuki düzenlemeler raporu. https://turkiye.ai/wp-content/uploads/2024/06/TRAI-Yapay-Zeka-Etik-Ilkeleri-ve-Hukuki-Duzenlemeler-Raporu-Mayis-2024-5.pdf
• Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., Tatsugami, F., Yanagawa, M., Hirata, K., Yamada, A., Tsuboyama, T., Kawamura, M., Fuzioka, T., & Naganawa, S. (2024). Fairness of artificial intelligence in healthcare: Review and recommendations. Japanese journal of radiology, 42(1), 3-15. https://doi.org/10.1007/s11604-023-01474-3
• Uraikul, V., Chan, C. W., & Tontiwachwuthikul, P. (2007). Artificial intelligence for monitoring and supervisory control of process systems. Engineering Applications of Artificial Intelligence, 20(2), 115-131.
• Vercellone, C. (2015). From the crisis to the ‘welfare of the common’ as a new mode of production. Theory, Culture & Society, 32(7-8), 85-99.
• Vilone, G., & Longo, L. (2021). Notions of explainability and evaluation approaches for explainable artificial intelligence. Information Fusion, 76, 89-106.
• Weber, M. (1958). The Protestant ethic and the spirit of capitalism. Scribner.
• Wemme, K. M., & Rosvall, M. (2005). Work related and non-work related stress in relation to low leisure time physical activity in a Swedish population. Journal of Epidemiology & Community Health, 59(5), 377-379. https://doi.org/10.1136/jech.2004.031526
• Winfield, A. F. (2019). On the simulation (and energy costs) of human intelligence, In The singularity. From astrophysics to unconventional computation (Vol. 35, pp. 397–407).
• Zhou, N., Zhang, Z., Nair, V. N., Singhal, H., & Chen, J. (2022). Bias, fairness and accountability with artificial intelligence and machine learning algorithms. International Statistical Review, 90(3), 468-480. https://doi.org/10.1111/insr.12492