Reklamcılıkta Kişisel Verilerin Korunması: "Algoritmik Rıza Yorgunluğu" ve Tüketici Otonomisinin Geri Kazanımı

Yıl 2025, Cilt: 2 Sayı: 2, 252 - 282, 26.07.2025

Pınar Yağmur Demir

Öz

Dijital çağda reklamcılık sektörü, kişisel verilerin yoğun kullanımıyla dönüşüme uğrarken, bu durum "Algoritmik Rıza Yorgunluğu" olarak adlandırılan yeni bir sorunu ortaya çıkarmıştır. Bu fenomen, bireylerin sürekli ve karmaşık rıza taleplerine maruz kalmaları sonucunda duyarsızlaşarak bilinçsizce onay verme eğilimi göstermelerini ifade eder. Çalışma, bu yorgunluğun bilişsel yüklenme ve karar verme yorgunluğu gibi psikolojik temellerini incelerken, dijital reklamcılık pratiklerinin (karmaşık çerez politikaları, mikro hedefleme, karanlık modeller) bu duruma nasıl katkıda bulunduğunu analiz etmektedir. Algoritmik rıza yorgunluğu, tüketici otonomisinin erozyonuna ve dijital ekosisteme olan güvenin azalmasına yol açmaktadır. Makale, bu kapsamlı soruna karşı çok boyutlu çözümler önermektedir: "Anlamlı rıza" ilkesini güçlendiren hukuki düzenlemeler, "varsayılan olarak gizlilik" ve "tasarımla gizlilik" ilkelerinin benimsenmesi, karanlık modellerin yasaklanması gibi düzenleyici yaklaşımlar incelenmektedir. Ayrıca, dinamik rıza yönetim sistemleri, yapay zeka destekli gizlilik asistanları ve blockchain tabanlı veri güven sandıkları gibi teknolojik yenilikler ele alınmaktadır. Son olarak, veri okuryazarlığının artırılması ve reklamcılık sektöründe etik sorumluluğun benimsenmesi gibi davranışsal ve etik çözümler vurgulanmaktadır. Bu çalışma, "Algoritmik Rıza Yorgunluğu"nu kapsamlı bir şekilde ele alarak, bireylerin dijital haklarını yeniden tesis etmek ve sürdürülebilir, etik bir dijital reklamcılık ortamı yaratmak için bir yol haritası sunmaktadır.

Anahtar Kelimeler

kişisel verilen korunması , rıza yorgunluğu , veri otonomisi , dijital reklamcılık , karanlık modeller

Advertising and Personal Data Protection: "Algorithmic Consent Fatigue" and the Reclamation of Consumer Autonomy

Öz

Digital advertising has undergone a significant transformation in the digital age, heavily relying on personal data. This evolution has, however, given rise to a new challenge: "Algorithmic Consent Fatigue." This phenomenon describes how individuals, overwhelmed by continuous and complex requests for consent, become desensitized and tend to grant permission unconsciously. This study examines the psychological underpinnings of this fatigue, such as cognitive overload and decision fatigue, and analyzes how current digital advertising practices, including complex cookie policies, microtargeting, and dark patterns, contribute to its exacerbation. Algorithmic consent fatigue ultimately leads to the erosion of consumer autonomy and a decline in trust within the digital ecosystem. The paper proposes multidimensional solutions to address this widespread issue. It explores regulatory approaches such as strengthening the principle of "meaningful consent," adopting "privacy by default" and "privacy by design" principles, and outright banning dark patterns. Furthermore, it delves into technological innovations like dynamic consent management systems, AI-powered privacy assistants, and blockchain-based data trust vaults. Finally, the study emphasizes behavioral and ethical solutions, including enhancing data literacy among consumers and promoting ethical responsibility within the advertising industry. By comprehensively addressing "Algorithmic Consent Fatigue," this research aims to provide a roadmap for restoring individuals' digital rights and fostering a sustainable, ethical digital advertising environment.

Anahtar Kelimeler

personal data protection , consent fatique , data autonomy , digital advertising , dark patterns

Kaynakça

• A.Obar, J., & Oeldorf-Hirsch, A. (2020). The Biggest Lie on the Internet: Ignoring the Privacy Policies and Terms of Service Policies of Social Networking Services. Information, Communication & Society, 23(1), 193-214.
• Acquisti, A., & Loewenstein, G. (2013). Privacy and Human Behavior in the Age of Information. Science, 340(6121), 249-250. Aïmeur, E., & Zhou, L. (2017). AI-Powered Privacy Assistants: A New Paradigm for User Privacy Management. Journal of Privacy and Confidentiality, 8(1), 88-105.
• Barocas, S., & Nissenbaum, H. (2014). Big Data's End Run Around Anonymity and Consent. Proceedings of the 2014 Conference on Computer Supported Cooperative Work and Social Computing (s. 34-42). Baltimore: Association for Computing Machinery.
• Barth, J., & De Jong, M. (2017). The Privacy Paradox: Investigating the Impact of Privacy Concerns and Privacy-Enhancing Technologies on Online Behavior. Computers in Human Behavior, 69, 114-124.
• Baumeister, R. F., Bratslavsky, E., & diğerleri. (1998). Ego Depletion: Is the Active Self a Limited Resource? Journal of Personality and Social Psychology, 74(5), 1252-1265.
• Bélanger, F., & Crossler, R. (2011). Privacy in the Digital Age: A Review of the Literature and Future Research Directions. Journal of the Association for Information Systems, 12(10), 133-156.
• Bennett, C. J. (2018). The Political Economy of Privacy: The Evolution of Privacy Governance in the Digital Age. Information, Communication & Society, 21(1), 77-92.
• Bennett, C. J., & Lyon, D. (2017). Privacy, Surveillance and the New Architecture of Power. Cambridge: Polity Press.
• BEUC, E. C. (2018). Dark Patterns: How Online Design Tricks Consumers. Brüksel.
• Boyd, D., & Crawford, K. (2012). Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon. Information, Communication & Society, 15(5), 662-679.
• Brignul, H. (2010). Dark Patterns: User interfaces designed to trick you. darkpatterns.org. darkpatterns.org: https://darkpatterns.org/ adresinden alındı
• Burton, D. (2021). Data Privacy: A Practical Guide for Business Leaders. New York: Kogan Page.
• Cavoukian, A. (2011). Privacy by Design: The 7 Foundational Principles. Toronto: Information and Privacy Commissioner of Ontario.
• Cranor, L. F. (2012). Confronting the Privacy Paradox: Privacy in the Digital Age. 2012 ACM Bilgisayar Destekli İşbirliği Çalışması (CSCW) Konferansı Bildirileri (s. 41-50). Baltimore: Association for Computing Machinery.
• Davidson, S., & De Filippi, P. (2016). Disrupting Governance: The New Institutional Economics of Distributed Ledger Technology. P. D. Filippi, & A. Wright içinde, The Blockchain and the Law: The Rule of Code (s. 50-70). Cambridge: Harvard University Press. EDPB, E. D. (2020). Guidelines 05/2020 on Consent under Regulation 2016/679. Brüksel: European Data Protection Board (EDPB).
• FTC, F. T. (2012). Protecting Consumer Privacy in an Era of Rapid Change: Recommendations for Businesses and Policymakers. Washington.
• Gellman, R. (2014). Privacy, Surveillance, and the Law. Santa Monica: RAND Corporation. Goodman, B., & Flaxman , S. (2017). European Union Regulations on Algorithmic Decision- Making and a 'Right to Explanation. AI Magazine, 38(3), 42-50.
• Gray, C. (2020). The Dark Side of UX: How Design Can Manipulate and Deceive. Cambridge: O'Reilly Media.
• ICAEW, I. o. (2019). Ethics and Data: The Challenges for Businesses. Londra: ICAEW. Johnson, E. J., & Bellman, S. (2002). Web Metrics: How Consumers Search for Product Information Online. Journal of Interactive Marketing, 16(2), 29-45.
• Joo-Heon, K. (2019). Personal Information Management Systems: A Review and Future Directions. Journal of Information Science Theory and Practice, 7(1), 70-82.
• Koops, B.-J. (2014). The Trouble with European Data Protection Law. International Data Privacy Law, 4(1), 69-82.
• Lynskey, O. (2017). The Foundations of EU Data Protection Law. Oxford: Oxford University Press.
• McDonald, A., & Cranor, L. (2008). The Cost of Reading Privacy Policies. Journal of Law and Policy for the Information Society, 4(4), 163-181.
• Metzger, M. J. (2007). Communication Privacy Management in the Public: The Dilemma of Online Privacy. Journal of Computer-Mediated Communication, 12(2), 358-378.
• Nissenbaum, H. (2010). Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford: Stanford University Press.
• Norberg, P. A., & Horne, D. (2007). The Privacy Paradox: Personal Information Disclosure Intentions versus Behaviors. Journal of Consumer Affairs, 41(1), 100-126.
• O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.
• Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge: Harvard University Press.
• Persily, M. (2017). The Internet and the Information Economy: Implications for Democracy. Daedalus, 146(1), 108-120. Rust, R. T., & Kannan, P. (2020). The Future of Service: Theories and Practices for the 21st Century. Cambridge: Cambridge University Press.
• Schoeman, F. D. (2006). Privacy and Social Freedom. Cambridge: Cambridge University Press. Solove, D. J. (2008). Understanding Privacy. Cambridge: Harvard University Press.
• Tapscott, D., & Tapscott, A. (2016). Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World. New York: Portfolio/Penguin.
• Turow, J. (2011). The Daily You: How the New Advertising Industry is Defining Your Identity and Your Worth. New Haven: Yale University Press.
• Utz, S. (2018). The Burden of Consent: Why Current Cookie Consent Mechanisms Fail and How to Improve Them. Journal of Internet Law, 22(1), 101-114.
• Veale, M., & Borgesius, F. (2018). Demystifying the GDPR's 'Right to Explanation. Computer Law & Security Review, 34(1), 110-125.
• Voigt, P., & Axel, V. (2017). The EU General Data Protection Regulation (GDPR): A Practical Guide. Heidelberg: Springer.
• Westin, A. F. (1967). Privacy and Freedom. New York: Atheneum.
• Zuboff, S. (2019). he Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs.