Dijital Medya Ortamında Rızanın Algoritmik İnşası: Eleştirel Algoritma Çalışmaları Perspektifinde Bir Tartışma

Öz

Bu çalışma, özellikle medyanın dahil olduğu dijital platformlardaki algoritmik yapının, bireysel ve toplumsal boyutlardaki ekonomik, kültürel, siyasal vb. potansiyel etkilerine odaklanarak iletişim çalışmalarına eleştirel algoritma çalışmaları terminolojisinden hareketle katkı sunmayı amaçlamaktadır. Algoritmik sistemlerin, her geçen gün daha fazla veri toplaması ve işlemesi ile kazandığı güç, dijital ortamdaki kullanıcıların çevrimiçi ya da çevrimdışı hareketliliklerini kapsayan verileri üzerinden ilerlemektedir. Bu bakımdan, algoritmalar ile çevrili dijital düzende bireylerin davranışlarının öngörülebilir olmasını mümkün kılan büyük veri kümelerinin işlenmesi ve analiz edilmesi ile yarattığı mevcut sorunlara ve risklere dikkat çeken araştırmacılar, eleştirel algoritma çalışmalarının doğuşunu sağlamıştır. Veriyi, yeni bir ekonomik değer olarak niteleyen, algoritma güdümlü dijital teknolojileri sosyal bilimsel bağlamları kapsamında değerlendiren eleştirel algoritma çalışmaları, bu çalışmanın kavramsal tartışmasının temel düşünsel dayanağını oluşturmaktadır. Algoritmik sistemlerin iletişim bilimi için önemi ise algoritmaların günlük hayatın pek çok yönünü önemli ölçüde etkilemesi, bu etki sürecini kullanıcıların dijital izleri boyunca “kara kutu” çalışma prensibine uygun biçimde çalışarak sürdürmesi ve kendi “kara kutu” çalışma prensiplerine karşın kullanıcılar hakkında çok şey biliyor olmaları durumu şeklinde özetlenebilmektedir. Bu nedenle çalışmada, dijital iletişim ortamında algoritma ve medya kullanıcısı arasındaki etkileşim, iletişim çalışmalarının eleştirel perspektifi ile ilişkilendirilerek eleştirel algoritma çalışmalarının risk tipolojilerinden biri olan gözetim pratikleri çerçevesinde algoritmik rıza kavramı bağlamında irdelenmiştir. İlgili alanyazınını kapsayan tartışmanın ardından eleştirel algoritma çalışmalarının ortaya koyduğu risk tipolojilerine karşın kullanıcı odaklı çözüm önerileri ışığında eleştirel algoritma farkındalığına yönelik önerilerde bulunulmuştur.

Anahtar Kelimeler

• algoritma
• makine öğrenmesi
• yapay zeka
• eleştirel algoritma çalışmaları
• algoritmik rıza

Algorithmic Construction of Consent in the Digital Media Environment: An Evaluation in the Perspective of Critical Algorithm Studies

Öz

This study aims to contribute to communication studies with the terminology of critical algorithm studies by focusing on the potential effects of the algorithmic structure in digital platforms, especially in the media, on economic, cultural, political, etc. in individual and social dimensions. The power that algorithmic systems gain by collecting and processing more and more data every day is based on the data covering the online or offline mobility of users in the digital environment. In this respect, researchers drawing attention to the existing problems and risks created by the processing and analysis of large data sets, which make it possible to predict the behaviour of individuals in the digital order surrounded by algorithms, have led to the emergence of critical algorithm studies. Critical algorithm studies, which characterise data as a new economic value and evaluate algorithm-driven digital technologies within their social scientific contexts, constitute the basic intellectual basis of the conceptual discussion of this study. The importance of algorithmic systems for communication science can be summarised as the fact that algorithms significantly affect many aspects of daily life, that they continue this influence process by working in accordance with the "black box" working principle along the digital traces of users, and that they know a lot about users despite their "black box" working principles. In this respect, in this study, the interaction between the algorithm and the media user in the digital communication environment is examined in the context of the concept of algorithmic consent within the framework of surveillance practices, one of the risk typologies of critical algorithm studies, in relation to the critical perspective of communication studies. Following the discussion of the relevant literature, suggestions were made for critical algorithm awareness in the light of user-oriented solution proposals despite the risk typologies put forward by critical algorithm studies.

Anahtar Kelimeler

• algorithm
• machine learning
• artificial intelligence
• critical algorithm studies
• algorithmic consent

Kaynakça

1. Acemoğlu, D. & Johnson, S. (2023). İktidar ve teknoloji: Bin yıllık mücadele. (C. Duran, Çev.). Doğan Kitap.
2. Aktan, E. (2018). Büyük Veri: Uygulama Alanları, Analitiği ve Güvenlik Boyutu. Bilgi Yönetimi Dergisi, 1(1), 1-22.
3. Barth, S. & de Jong, T. D. M. (2017). The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior – A systematic literature review. Telematics and Informatics, 34(7), 1038-1058.
4. Bauman, Z. (2019). Akışkan modernite. (S. O. Çavuş, Çev.). Can Yayınları.
5. Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59.
6. Bennett, J. C. (2001). Cookies, web bugs, webcams and cue cats: Patterns of surveillance on the world wide web. Ethics and Information Technology. 3, 197- 210.
7. Berners- Lee, T. (2000). Weaving the eeb. Harper Bussiness.
8. Blazquez, D. & Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change. 130(C), 99-113.

9. Bloom, P. (2019). Monitored: business and surveillance in a time of big data. Pluto Press.
10. Boyd, D. & Crawford, K. (2011, 13 Eylül). Six Provocations for Big Data. [Sempozyum Bildirisi] A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, Oxford, İngiltere, https://ssrn.com/abstract=1926431
11. Bozdag E. (2013). Bias in algorithmic filtering and personalization. Ethics and Information Technology, 15, 209- 227.
12. Bucher, T. (2017). The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information Communication and Society, 20(1), 30–44.
13. Bulut, S. (2020). Dijital çağda medya: makine öğrenmesi, algoritmik habercilik ve gazetecilikte işlevsiz insan sorunsalı. Selçuk İletişim, 13(1), 294-313.
14. Burr C, Cristianini N, Ladyman J (2018) An analysis of the interaction between intelligent software agents and human users. Mind Mach, 28(4), 735–774.
15. Castells, M. (2004). The network society. Edward Elgar.
16. Chen, W., & Quan-Haase, A. (2018). Big data ethics and politics: Toward new understandings. Social Science Computer Review, 38(1), doi:10.1177/0894439318810734
17. Cheney-Lippold, J. (2011). A new algorithmic identity. Theory, Culture & Society, 28(6), 164-181.
18. Chorianopoulos, K. (2008). Personalized and mobile digital TV applications. Multimedia Tools and Applications, 36, 1-10.
19. Christin, S., Hervet, É., & Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution. doi:10.1111/2041-210x.13256
20. Clarke, R. (1988). Information technology and dataveillance. Communications of the ACM. 31(5), 498-512.
21. Clerwall, C. (2014). Enter the robot journalist. Journalism Practice, 8(5), 519–531.
22. Cobbe, J. & Singh, J. (2019). Regulating recommending: Motivations, considerations, and principles. European Journal of Law and Technology, 10(3). http://dx.doi.org/10.2139/ssrn.3371830
23. Çınar, N. & Ateş, S. (2022). Data privacy in digital advertising: Towards a post third-party cookie era. M. Filimowicz (Ed.) Privacy: Algorithms and Society. Routledge.
24. Çoban, B. (2019). Gözün iktidarı üzerine. B. Çoban, Z. Özarslan (Haz.), Panoptikon: Gözün iktidarı (ss. 111-137). Su Yayınları.
25. Denny, J. (2020). What is an algorithm? How computers know what to do with data. https://theconversation.com/what-is-an-algorithm-how-computers-know-what-to-do-with-data-146665
26. Desai, D. (2017). A study of design aspects of web personalization for online users in India. [Doktora Tezi]. Gujarat Technological University, India.
27. Diakopoulos N. (2015). Algorithmic accountability. Digital Journalism, 3(3), 398-415.
28. Diakopoulos, N. (2016). Accountability in algorithmic decision making, Communications of the ACM, 59(2), 56- 62.
29. DiFranzo, D., & Gloria-Garcia, K. (2017). Filter bubbles and fake news. XRDS: Crossroads, The ACM Magazine for Students, 23(3), 32–35.
30. Dolgun, U. (2005). Enformasyon toplumundan gözetim toplumuna 21. yüzyılda gözetim, toplumsal denetim ve iktidar ilişkileri. Ekin Kitabevi.
31. Fırat, F. (2019). Robot journalism. The International Encyclopedia of Journalism Studies, 1–5. doi:10.1002/9781118841570.iejs02
32. Fletcher, R. (2020). The truth behind filter bubbles: Bursting some myths. Reuters Institute. https://reutersinstitute.politics.ox.ac.uk/news/truth-behind-filter-bubbles-bursting-some-myths
33. Forbes (2022). The world’s largest tech companies in 2022: Apple still dominates as brutal market selloff wipes trillions in market value. https://www.forbes.com/sites/jonathanponciano/2022/05/12/the-worlds-largest-technology-companies-in-2022-apple-still-dominates-as-brutal-market-selloff-wipes-trillions-in-market-value/?sh=15b4b78e3448
34. Fouquaert, T. (2019). The algorithm paradox: Knowledge of versus attitudes towards curation algorithms, [Yüksek Lisans Tezi], Ghent University, Ghent, Belgium.
35. Fouquaert, T., & Mechant, P. (2021). Making curation algorithms apparent: a case study of “Instawareness” as a means to heighten awareness and understanding of Instagram’s algorithm. Information, Communication & Society, 1–21.
36. Fuchs, C. (2019). Karl Marx in the Age of Big Data Capitalism. D. Chandler & C. Fuchs (eds.) Digital objects, digital subjects: Interdisciplinary perspectives on capitalism, labour and politics in the age of big data (ss. 53–71). University of Westminster Press.
37. Giddens, A. (2016). Modernliğin sonuçları. (E. Kuşdil, Çev.). Ayrıntı.
38. Gillespie, T. (2014). The relevance of algorithms. Media technologies: Essays on communication, T. Gillespie, P. J. Boczkowski, K. A. Foot (Ed.), Materiality and society (ss. 167- 193) MIT Press.
39. Gillespie, T. & Seaver, N. (2016). Critical algorithm studies: a reading list. https://socialmediacollective.org/reading-lists/critical-algorithm-studies/
40. Gürsakal, N. (2021). Makine öğrenmesi. Dora.
41. Han, B. C. (2019). Psikopolitika: Neoliberalizm ve yeni iktidar teknikleri. (H. Barışçan, Çev.), Metis. Herschel, R., & Miori, V. M. (2017). Ethics & Big data. Technology in Society, 49, 31–36.
42. Iliadis, A. & Russo, F. (2016). Critical data studies: An introduction. Big Data & Society, 3(2), doi:10.1177/2053951716674238
43. Jenkins H. (2004) Medya yöndeşmesinin kültürel mantığı. (E. Cizmeci, Çev.) F. Aydoğan (Ed.) Yeni medya kuramları. 33- 44.
44. Kant, T. (2021). Making it personal: Algorithmic personalization, identity, and everyday life. Oxford University Press.
45. Karakoç Keskin, E. (2023). Eleştirel algoritma çalışmaları perspektifinde dijital platformlarda öneri sistemleri: Kullanıcı motivasyonlarına yönelik bir araştırma. [Doktora Tezi], İstanbul Üniversitesi.
46. Karimi M, Jannach D, Jugovac M (2018) News recommender systems—survey and roads ahead. Inf Process Manag 54(6), 1203– 1227.
47. Kaya Erdem, B. (2021). Yapay zekanın medya ve yayıncılık alanına etkisi. TRT Akademi. 6(13), 897- 903.
48. Kim, D., & Kim, S. (2018). Newspaper journalists’ attitudes towards robot journalism. Telematics and Informatics, 35(2), 340–357.
49. Kitchin, R. (2014). The data revolution big data, open data, aata infrastructures &The consequences. Sage. Kotler, P., Kartajaya, H. & Setiawan, I. (2021). Pazarlama 5.0 insan için teknoloji. (T. Gezer, Çev.). Nişantaşı Üniversitesi Yayınları.
50. Kraemer, F., Van Overveld, K., & Peterson, M. (2011). Is there an ethics of algorithms? Ethics and Information Technology, 13(3), 251–260.
51. Kröger, L. J., Lutz, H-M., O. & Müller, F. (2020). What does your gaze reveal about you? On the privacy implications of eye tracking. M. Friedewald, M. Önen, E. Lievens, S. Krenn, S. Fricker (Eds.) Privacy and identity management: Data for better living: AI and privacy: Revised selected papers (ss. 226-241). Springer
52. Latar, L. N. (2018). Robot journalism: Can human journalism survive? World Scientific Publishing. Lee, K. (2018). Yapay zekâ ve yeni dünya düzeni Çin Silikon Vadisi. (Ü. Şensoy & L. Göktem, Çev). MyTECHNIC/ Optimist Yayın Grubu.
53. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
54. Løkke, E. (2020). Mahremiyet dijital toplumda özel hayat. (D. Başak, Çev.), Koç Üniversitesi Yayınları.
55. Lyon, D. (1994). The electronic eye rise of surveillance society. University of Minnesota Press.
56. Lyon, D. (2003). Surveillance as social sorting Computer codes and mobile bodies. D. Lyon (Ed.) Surveillance as social sorting: Privacy, risk, and digital discrimination (ss. 13-30). Routledge.
57. Marciano, A., Nicita, A. & Ramello, B. G. (2020). Big data and big techs: understanding the value of information in platform capitalism. European Journal of Law and Economics, 50, 345–358
58. Marcus, G. (2017). Deep learning: A critical appraisal. https://arxiv.org/pdf/1801.00631
59. Manovich, L. (2002). The language of new media. The MIT Press.
60. Mathiesen, T. (1997). The viewer society. Theoretical Criminology, 1(2), 215–234. Markham, A., Stavrova, S. & Schlüter, M. (2018). Netflix, imagined affordances, and the illusion of control. T. Plothe & A.M. Buck (eds), Netflix at the nexus: Content, practice and production in the age of streaming television (ss. 29-46) Peter Lang.
61. Martin K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160, 835- 850.
62. Marx, G.T. (2002). What’s new about the “new surveillance”? Classifying for change and continuity. Know Techn Pol 17, 18–37.
63. McLellan, D. (2000). Karl Marx selected writings. Oxford University Press.
64. Moats, D. & Seaver, N. (2019). “You social scientists love mind games”: Experimenting in the “divide” between data science and critical algorithm studies, Big Data & Society, 1-11. https://doi.org/10.1177/2053951719833404 Morozov, E. (2011). The net delusion: The dark side of internet freedom. Public Affairs.
65. Nagy, P. & Neff, G. (2015). Imagined affordance: Reconstructing a keyword for communication theory. Social Media + Society, 1(2), 1-9.
66. Naik, K., & Joshi, A. (2017, 10-11 Şubat). Role of Big Data in Various Sectors. [Konferans Bildirisi] 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). Palladam, India, doi:10.1109/i-smac.2017.8058321
67. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
68. O’Neil, C. (2017). The ivory tower can’t keep ignoring tech. New York Times, www.nytimes.com/2017/11/14/opinion/academia-tech-algorithms.html
69. Özarslan, Z. (2019). Gözün iktidarı: Elektronik gözetim sistemleri. B. Çoban, Z. Özarslan (Haz.), Panoptikon: Gözün iktidarı (ss. 139-154). Su Yayınları.
70. Palmas, K. (2011). Predicting what you’ll do tomorrow: Panspectric surveillance and the contemporary corporation, Surveillance & Society, 8(3), 338-354.
71. Pariser, E. (2011). The filter bubble what the internet hiding from you, Penguin Press.
72. Poster, M. (1989). Critical theory and poststructuralism in search of a context. Cornell University Press.
73. Rogers, M. E. (1989). Communication technology: The new media in society. The Free Press.
74. Rosen, J. (2004). The naked crowd reclaimıng security and freedom in an anxious age. http://www.antoniocasella.eu/nume/rosen_2004.pdf
75. Ruckenstein, M., & Granroth, J. (2019). Algorithms, advertising and the intimacy of surveillance. Journal of Cultural Economy, 1–13. doi:10.1080/17530350.2019.1574866 Schick, A. G., Gordon, L. A. & Haka, S. (1990). Information overload: A temporal approach. accounting, Organizations and Society, 15(3), 199–220.
76. Schumann, C., & Taddicken, M. (2021). Algorithms as research objects for communication science. M. Taddicken & C. Schumann (Eds.), Algorithms and communication (ss. 7-23). https://doi.org/10.48541/dcr.v9.1
77. Share Lab. (2015). Invisible infrastructures: Online trackers. https://labs.rs/en/invisibleinfrastructures-online-trackers
78. Smith, M., Szongott, C., Henne, B., & von Voigt, G. (2012, 18-20 Haziran). Big Data Privacy Issues in Public Social Media. [Konferans Bildirisi] 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST). Campione d'Italia, İtalya. https://ieeexplore.ieee.org/document/6227909
79. Schulz, C. (2022). A new algorithmic imaginary. Media, Culture & Society. https://doi.org/10.1177/0163443722113601
80. Solove, D. J. (2021). The myth of the privacy paradox. George Washington Law Review, 89(1), 1-51.
81. Songar, A. (1979). Sibernetik. Yeni Asya Yayınları.
82. Sunstein, C. (2001). Echo chambers: Bush v. Gore, impeachment, and beyond. Princton University Press. Taşkaya, M. & Talay, Ö. (2019). Dijital gözetimin pazarlama amaçlı aracıları: “Çerezler” ve çerez kullanımında “açık rıza.” Akdeniz Üniversitesi İletişim Fakültesi Dergisi, 31, 356-376.
83. Toffler, A. (1980). The third wave. Bantam.
84. Treré, E. (2016). The dark side of digital politics: Understanding the algorithmic manufacturing of consent and the hindering of online dissidence. IDS Bulletin Transforming Development Knowledge, 47(1), 127-139.
85. Turing M. A. (1950). Compuing machinery and intelligence. Mind, 236, 433- 460.
86. Uluk, M. (2021). Veri odaklı kapitalizmin ardındakiler: Aktörler, değişen iş modelleri ve algoritmaların rolü. O. Kuş (Ed.), Algoritmaların gölgesinde toplum ve iletişim (ss. 99-117). Alternatif Bilişim.
87. Van Dijk, J. (2006). The network society. Sage.
88. Van Dijck, J. (2014). Datafication, dataism and dataveillance: big data between scientific paradigm and ideology. Surveillance and Society, 12(2), 197–208.
89. Van Dijck, J. (2017). Foreword. Schäfer, T. M. & van Es, K. (Eds.). The datafied society studying culture through data (ss. 12- 13). Amsterdam University Press.
90. Van Dalen, A. (2012). The algorithms behind the headlines. Journalism Practice, 6(5-6), 648-658.
91. West, S.M. (2017). Data capitalism: Redefining the logics of surveillance and privacy. Business & Society. doi:10.1177/0007650317718185
92. Westin, A. (1967). Privacy and freedom. IG Publishing.
93. William, J. (2022). Learn about the upcoming techniques web 3.0 vs web 4.0. https://www.temok.com/blog/web-30-vs-web-40/ (13.08.2022).
94. Zengin, F. (2020). Akıllı makine çağı sinemasına giriş: Sinema sanatında yapay zekâ teknolojilerinin kullanımı. İletişim Çalışmaları Dergisi, 6(2), 151- 177.
95. Zweig, K. (2019). Martin Orth tarafından gerçekleştirilen röportaj: “İyi algoritma, kötü algoritma”. https://www.deutschland.de/tr/topic/ekonomi/dijtallesme-algortimalar-hayatimizi-kolaylastiriyor-mu-yoksa-birer-tehlike-mi
96. Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), doi:10.1177/2053951714559253
97. Zuboff, S. (2015). Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30, 75-89.
98. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Public Affairs.