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Journals / Beykoz Akademi Dergisi / 2025 / Cilt: 13 Sayı: 2

Analyzing the Confluence of Algorithmic Transparency, Accountability, and Data Privacy: A Comprehensive Study on Trust Dynamics in AI Systems

Algoritmik Şeffaflık, Hesap Verebilirlik ve Veri Gizliliğinin Birleşimini Analiz Etmek: Yapay Zeka Sistemlerinde Güven Dinamikleri Üzerine Kapsamlı Bir Çalışma

Erdal Büyükbıçakcı(SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, BİLİŞİM TEKNOLOJİLERİ MYO, BİLGİSAYAR TEKNOLOJİLERİ BÖLÜMÜ)0000-0001-7276-741X
Pages
440–469
Publication Date
2025-12-17
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AI Summary & Key Takeaways

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

This study not only enriches the existing body of literature by bridging several research gaps but also provides concrete recommendations for policymakers and practitioners aiming to enhance public trust in AI systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The increasing ubiquity of Artificial Intelligence (AI) systems in daily life brings the issue of public trust to the forefront of technological and ethical discussions.
📊
Key Findings & Data:Using Structural Equation Modeling (SEM) on a dataset of 398 respondents, our findings significantly supported all four hypotheses.
💡
Conclusion & Impact:This study not only enriches the existing body of literature by bridging several research gaps but also provides concrete recommendations for policymakers and practitioners aiming to enhance public trust in AI systems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zeka · Güven · Algoritmik Şeffaflık

Abstract

The increasing ubiquity of Artificial Intelligence (AI) systems in daily life brings the issue of public trust to the forefront of technological and ethical discussions. This study aims to examine how Algorithmic Transparency (AT), Accountability Mechanisms (AM), and Data Privacy Policies (DPP) influence Trust in AI Systems (TAS). Drawing on Social Cognitive Theory, we hypothesized that AT, AM, and DPP would positively correlate with TAS, with DPP acting as a moderator between AT and TAS. Using Structural Equation Modeling (SEM) on a dataset of 398 respondents, our findings significantly supported all four hypotheses. Notably, DPP was found to moderate the relationship between AT and TAS, emphasizing its role as a critical component in shaping public trust. This study not only enriches the existing body of literature by bridging several research gaps but also provides concrete recommendations for policymakers and practitioners aiming to enhance public trust in AI systems.

Özet

Yapay Zeka (YZ) sistemlerinin günlük hayatta giderek yaygınlaşması, toplumsal güven konusunu teknolojik ve etik tartışmalarda ön plana çıkarmaktadır. Bu çalışma, Algoritmik Şeffaflık (AŞ), Hesap Verebilirlik Mekanizmaları (HVM) ve Veri Gizliliği Politikalarının (VGP) Yapay Zeka Sistemlerine Güveni (YSG) nasıl etkilediğini incelemeyi amaçlamaktadır. Bu amaçla, Sosyal Bilişsel Teori'ye dayanarak, AŞ, HVM ve VGP'nin YSG ile pozitif korelasyon göstereceği ve VGP'nin AŞ ile YSG arasında moderatör görevi göreceği öngörülmüştür. Bulgular, 398 katılımcıdan oluşan bir veri kümesi üzerinde Yapısal Eşitlik Modellemesi (YEM) kullanarak dört hipotezin tümünü önemli ölçüde desteklemiştir. Özellikle, VGP'nin AŞ ve YSG arasındaki ilişkiyi ılımlı hale getirdiği tespit edilmiş ve toplumsal güveni şekillendirmede kritik bir bileşen olarak rol oynadığı vurgulanmıştır. Bu çalışma, çeşitli araştırma boşluklarını kapatarak mevcut literatürü zenginleştirmekle kalmayıp, aynı zamanda YZ sistemlerinde kamu ve toplumsal güveni artırmayı amaçlayan politika yapıcılar, uygulayıcılar için de somut öneriler sunmaktadır.

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Cite This Article

Büyükbıçakcı, E. (2025). Analyzing the Confluence of Algorithmic Transparency, Accountability, and Data Privacy: A Comprehensive Study on Trust Dynamics in AI Systems. Beykoz Akademi Dergisi, Cilt: 13 Sayı: 2, 440–469. https://doi.org/10.14514/beykozad.1746992

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References (69)

  1. Abdurohman, N. R. (2025). Artificial intelligence in higher education: Opportunities and challenges. Eurasian Science Review An International Peer-Reviewed Multidisciplinary Journal, 2(Special Issue), 1683–1695. https://doi.org/10.63034/esr-334
  2. Adadi, A. & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
  3. AGS Global. (2023). Dijital dönüşümün iş süreçlerine etkileri. MÜSİAD. https://www.musiad.org.tr/uploads/yayinlar/arastirma-raporlari/pdf/ags-global_musiad-dijital-donusumun-is-sureclerine-etkileri.pdf
  4. AlDhaen, F. (2022). The use of artificial intelligence in higher education – systematic review. In COVID-19 Challenges to University Information Technology Governance (pp. 269–285). Springer International Publishing. https://doi.org/10.1007/978-3-031-13351-0_13
  5. Ali, S., Akhlaq, F., Imran, A. S., Kastrati, Z., Daudpota, S. M. & Moosa, M. (2023). The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review. Computers in Biology and Medicine, 166, 107555. https://doi.org/10.1016/j.compbiomed.2023.107555
  6. Alomary, A., & Woollard, J. (2015). How is technology accepted by users? A review of technology acceptance models and theories. 5th International Conference on 4E, 1–4.
  7. Altuntaş, H., & Karabay, E. (2024). Üniversite öğrencileri ve öğretim üyelerinin yapay zekâya ilişkin metaforik algıları. Yönetim Bilişim Sistemleri Dergisi, 10(2), 35–52.
  8. Asıl, S. (2025). Yapay zekâ etiği: Temel ilkeler, sorunlar ve disiplinlerarası yaklaşımlar. İNİF E- Dergi, 10(1), 152–175.
  9. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
  10. Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.
  11. Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., & Allas, T. (2017). Artificial intelligence: The next digital frontier?McKinsey Global Institute. https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx
  12. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512
  13. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage.
  14. Digital Education Council. (2025). Digital Education Council global AI faculty survey 2025. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-faculty-survey
  15. Doruköz, K. D., & Uslu, B. (2023). Yapay zekânın iş hayatındaki yeri: Avantajlar, dezavantajlar ve politikalar. Bandırma Onyedi Eylül Üniversitesi Sosyal Bilimler Araştırmaları Dergisi, 6(CEEİK 2023 Özel Sayısı), 45–62. https://doi.org/10.38120/banusad.1376452
  16. Edelman. (2022). 2022 Edelman trust barometer: Global report. https://www.edelman.com/sites/g/files/aatuss191/files/2022-01/2022%20Edelman%20Trust%20Barometer%20Global%20Report_Final.pdf
  17. Evans, J. R., & Mathur, A. (2018). The value of online surveys: A look back and a look ahead. Internet Research, 28(4), 854–887. https://doi.org/10.1108/IntR-03-2018-0089
  18. EY Türkiye. (2025, May 29). Yapay zekâ duyarlılık endeksi sonuçları. Fintech Istanbul. https://fintechistanbul.org/2025/05/30/ey-yapay-zeka-duyarlilik-endeksi-sonuclari-aciklandi/
  19. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.
  20. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  21. Gerli, P., Clement, J., Esposito, G., Mora, L. & Crutzen, N. (2022). The hidden power of emotions: How psychological factors influence skill development in smart technology adoption. Technological Forecasting and Social Change, 180, 121721. https://doi.org/10.1016/j.techfore.2022.121721
  22. Grimmelikhuijsen, S. & Meijer, A. (2022). Legitimacy of algorithmic decision-making: Six threats and the need for a calibrated institutional response. Perspectives on Public Management and Governance, 5(3), 232–242. https://doi.org/10.1093/ppmgov/gvac008
  23. Guastella, A., & Seçkin, E. (2025). GenAI: Ready or not? Perspectives on talent, leadership, and cultural transformation. TÜSİAD. https://tusiad.org/tr/yayinlar/raporlar/item/11816-genai-ready-or-not-perspectives-on-talent-leadership-and-cultural-transformation
  24. Gustilo, L., Ong, E. & Lapinid, M. R. (2024). Algorithmically-driven writing and academic integrity: Exploring educators’ practices, perceptions, and policies in AI era. International Journal for Educational Integrity, 20(1), 3. https://doi.org/10.1007/s40979-024-00153-8
  25. Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
  26. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021b). Partial least squares structural equation modeling (PLS-SEM) using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
  27. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  28. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about PLS. Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928
  29. Herdiani, A., Mahayana, D., & Rosmansyah, Y. (2024). Building trust in an artificial intelligence-based educational support system: A narrative review. Jurnal Sosioteknologi, 23(1), 101–119. https://doi.org/10.5614/sostek.itbj.2024.23.1.6
  30. Hosain, M. T., Anik, M. H., Rafi, S., Tabassum, R., Insia, K., & Siddiky, M. M. (2023). Path to gain functional transparency in artificial intelligence with meaningful explainability. Journal of Metaverse, 3(2), 166–180. https://doi.org/10.57019/jmv.1306685
  31. Kim, T. W., & Routledge, B. R. (2022). Why a right to an explanation of algorithmic decision-making should exist: A trust-based approach. Business Ethics Quarterly, 32(1), 75–102. https://doi.org/10.1017/beq.2021.3
  32. Knowles, B., & Richards, J. T. (2021). The sanction of authority. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 262–271. https://doi.org/10.1145/3442188.3445890
  33. Kock, F., Berbekova, A., & Assaf, A. G. (2021). Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, 104330. https://doi.org/10.1016/j.tourman.2021.104330
  34. Kruse, L., Wunderlich, N., & Beck, R. (2019). Artificial intelligence for the financial services industry: What challenges organizations to succeed? Proceedings of the Annual Hawaii International Conference on System Sciences, 6408–6417. http://hdl.handle.net/10125/60075
  35. Levene, H. (1960). Robust tests for equality of variances. In I. Olkin (Ed.), Contributions to probability and statistics: Essays in honor of Harold Hotelling (pp. 278–292). Stanford University Press.
  36. Malik, A., & Budhwar, P. (2023). Artificial intelligence and international HRM. Routledge. https://doi.org/10.4324/9781003377085
  37. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335
  38. McLeod, A., & Dolezel, D. (2022). Information security policy non-compliance: Can capitulation theory explain user behaviors? Computers & Security, 112, 102526. https://doi.org/10.1016/j.cose.2021.102526
  39. Meylani, R. (2024). Artificial intelligence in the education of teachers: A qualitative synthesis of the cutting-edge research literature. Journal of Computer and Education Research, 12(24), 600–637. https://doi.org/10.18009/jcer.1477709
  40. Michael, K., Kobran, S., Abbas, R., & Hamdoun, S. (2019). Privacy, data rights and cybersecurity: Technology for good in the achievement of sustainable development goals. 2019 IEEE International Symposium on Technology and Society (ISTAS), 1–13. https://doi.org/10.1109/ISTAS48451.2019.8937956
  41. Mikalef, P., & Pateli, A. (2017). Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA. Journal of Business Research, 70, 1–16. https://doi.org/10.1016/j.jbusres.2016.09.004
  42. Ministry of National Education (MEB). (2025). Artificial Intelligence and Big Data Applications Department. Retrieved May 15, 2025, from https://yegitek.meb.gov.tr/www/yapay-zek-ve-buyuk-veri-uygulamalari-daire-baskanligi/icerik/3783
  43. Novelli, C., Taddeo, M., & Floridi, L. (2024). Accountability in artificial intelligence: What it is and how it works. AI & Society, 39(4), 1871–1882. https://doi.org/10.1007/s00146-023-01635-y
  44. Omrani, N., Rivieccio, G., Fiore, U., Schiavone, F., & Agreda, S. G. (2022). To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts. Technological Forecasting and Social Change, 181, 121763. https://doi.org/10.1016/j.techfore.2022.121763
  45. Personal Data Protection Authority. (2025). Yapay zeka ve kişisel verilerin korunması rehberi. https://www.kvkk.gov.tr/Icerik/7048/Yapay-Zeka-Alaninda-Kisisel-Verilerin-Korunmasina-Dair-Tavsiyeler
  46. Pleyers, G., & Poncin, I. (2020). Non-immersive virtual reality technologies in real estate: How customer experience drives attitudes toward properties and the service provider. Journal of Retailing and Consumer Services, 57, 102175. https://doi.org/10.1016/j.jretconser.2020.102175
  47. Radanliev, P. (2025). AI ethics: Integrating transparency, fairness, and privacy in AI development. Applied Artificial Intelligence, 39(1). https://doi.org/10.1080/08839514.2025.2463722
  48. Ramayah, T., Cheah, J.-H., Chuah, F., Ting, H., & Memon, M. A. (2017). Assessment of moderation analysis. In Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0 (Ch. 13). Pearson.
  49. Republic of Türkiye Ministry of Industry and Technology. (2021). National artificial intelligence strategy of Turkey 2021–2025. https://www.turkiye.ai/ulusal-yapay-zeka-stratejisi-2021-2025.pdf
  50. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. SmartPLS GmbH. http://www.smartpls.com
  51. Robinson, S. C. (2020). Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI). Technology in Society, 63, 101421. https://doi.org/10.1016/j.techsoc.2020.101421
  52. Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003
  53. Sarstedt, M., Ringle, C. M., Cheah, J.-H., Ting, H., Moisescu, O. I., & Radomir, L. (2020). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554. https://doi.org/10.1177/1354816618823921
  54. Savaş, S. (2021). Artificial intelligence and innovative applications in education: The case of Turkey. Journal of Information Systems and Management Research, 3(1), 14–26.
  55. Schmidt, P., Biessmann, F., & Teubner, T. (2020). Transparency and trust in artificial intelligence systems. Journal of Decision Systems, 29(4), 260–278. https://doi.org/10.1080/12460125.2020.1819094
  56. Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2022). Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy. Government Information Quarterly, 39(4), 101624. https://doi.org/10.1016/j.giq.2021.101624
  57. Shin, D. (2020). User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 64(4), 541–565. https://doi.org/10.1080/08838151.2020.1843357
  58. Slimi, Z., & Carballido, B. V. (2023). Navigating the ethical challenges of artificial intelligence in higher education: An analysis of seven global AI ethics policies. TEM Journal, 12(2). https://doi.org/10.18421/TEM122-02U
  59. Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, 31(2), 447–464. https://doi.org/10.1007/s12525-020-00441-4
  60. Trust, T., Whalen, J., & Mouza, C. (2023). Editorial: ChatGPT: Challenges, opportunities, and implications for teacher education. Contemporary Issues in Technology and Teacher Education, 23(1), 1–23. https://www.learntechlib.org/primary/p/222408/
  61. Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2022). The ethics of algorithms: Key problems and solutions. AI & Society, 37(1), 215–230. https://doi.org/10.1007/s00146-021-01154-8
  62. Turkish Informatics Association. (2023). 3. Kişisel verileri koruma zirvesi sonuç raporu. https://www.tbd.org.tr/pdf/raporlar/3-kvkk-zirvesi-sonuc-raporu.pdf
  63. Vakifli, I. (2025). Bütüncül çerçevede yapay zeka: Dünyadan ve Türkiye'den örnekler. İş'te Davranış Dergisi, 10(1), 1–29. https://doi.org/10.25203/idd.1688911
  64. Wahyuni, F., Wiyono, B. B., Atmoko, A., & Hambali, İ. (2019). Assessing relationships between emotional intelligence, school climate and school counselors burnout: A structural equation model. Journal for the Education of Gifted Young Scientists, 7(4), 1361–1374. https://doi.org/10.17478/jegys.639397
  65. Wright, K. B. (2005). Researching internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer-Mediated Communication, 10(3), JCMC1034. https://doi.org/10.1111/j.1083-6101.2005.tb00259.x
  66. Yang, E., & Beil, C. (2024). Ensuring data privacy in AI/ML implementation. New Directions for Higher Education, 2024(207), 63–78. https://doi.org/10.1002/he.20509
  67. Yeşilyurt, S., Dündar, R., & Demir, R. Z. (2024). Türkiye’de yapay zekâ ve eğitim ilişkisini inceleyen lisansüstü tezlerin analizi: Bir meta sentez çalışması. Journal of Innovative Research in Social Studies, 7(1), 47–73. https://doi.org/10.47503/jirss.1484848
  68. YÖK. (2025). YÖK veri yönetim sistemi. Retrieved April 11, 2025, from https://veriyonetim.yok.gov.tr/
  69. Zickar, M. J., & Keith, M. G. (2023). Innovations in sampling: Improving the appropriateness and quality of samples in organizational research. Annual Review of Organizational Psychology and Organizational Behavior, 10(1), 315–337. https://doi.org/10.1146/annurev-orgpsych-120920-052946