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

Yıl 2024, Sayı: 16, 329 - 353, 29.06.2024

Elif Karakoç Keskin

https://doi.org/10.55609/yenimedya.1424585

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

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  • Bloom, P. (2019). Monitored: business and surveillance in a time of big data. Pluto Press.
  • 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
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  • Bucher, T. (2017). The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information Communication and Society, 20(1), 30–44.
  • Bulut, S. (2020). Dijital çağda medya: makine öğrenmesi, algoritmik habercilik ve gazetecilikte işlevsiz insan sorunsalı. Selçuk İletişim, 13(1), 294-313.
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Dijital Medya Ortamında Rızanın Algoritmik İnşası: Eleştirel Algoritma Çalışmaları Perspektifinde Bir Tartışma

Yıl 2024, Sayı: 16, 329 - 353, 29.06.2024

Elif Karakoç Keskin

https://doi.org/10.55609/yenimedya.1424585

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

Kaynakça

  • Acemoğlu, D. & Johnson, S. (2023). İktidar ve teknoloji: Bin yıllık mücadele. (C. Duran, Çev.). Doğan Kitap.
  • Aktan, E. (2018). Büyük Veri: Uygulama Alanları, Analitiği ve Güvenlik Boyutu. Bilgi Yönetimi Dergisi, 1(1), 1-22.
  • 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.
  • Bauman, Z. (2019). Akışkan modernite. (S. O. Çavuş, Çev.). Can Yayınları.
  • Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59.
  • 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.
  • Berners- Lee, T. (2000). Weaving the eeb. Harper Bussiness.
  • Blazquez, D. & Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change. 130(C), 99-113.
  • Bloom, P. (2019). Monitored: business and surveillance in a time of big data. Pluto Press.
  • 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
  • Bozdag E. (2013). Bias in algorithmic filtering and personalization. Ethics and Information Technology, 15, 209- 227.
  • Bucher, T. (2017). The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information Communication and Society, 20(1), 30–44.
  • Bulut, S. (2020). Dijital çağda medya: makine öğrenmesi, algoritmik habercilik ve gazetecilikte işlevsiz insan sorunsalı. Selçuk İletişim, 13(1), 294-313.
  • 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.
  • Castells, M. (2004). The network society. Edward Elgar.
  • Chen, W., & Quan-Haase, A. (2018). Big data ethics and politics: Toward new understandings. Social Science Computer Review, 38(1), doi:10.1177/0894439318810734
  • Cheney-Lippold, J. (2011). A new algorithmic identity. Theory, Culture & Society, 28(6), 164-181.
  • Chorianopoulos, K. (2008). Personalized and mobile digital TV applications. Multimedia Tools and Applications, 36, 1-10.
  • Christin, S., Hervet, É., & Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution. doi:10.1111/2041-210x.13256
  • Clarke, R. (1988). Information technology and dataveillance. Communications of the ACM. 31(5), 498-512.
  • Clerwall, C. (2014). Enter the robot journalist. Journalism Practice, 8(5), 519–531.
  • 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
  • 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.
  • Çoban, B. (2019). Gözün iktidarı üzerine. B. Çoban, Z. Özarslan (Haz.), Panoptikon: Gözün iktidarı (ss. 111-137). Su Yayınları.
  • 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
  • Desai, D. (2017). A study of design aspects of web personalization for online users in India. [Doktora Tezi]. Gujarat Technological University, India.
  • Diakopoulos N. (2015). Algorithmic accountability. Digital Journalism, 3(3), 398-415.
  • Diakopoulos, N. (2016). Accountability in algorithmic decision making, Communications of the ACM, 59(2), 56- 62.
  • DiFranzo, D., & Gloria-Garcia, K. (2017). Filter bubbles and fake news. XRDS: Crossroads, The ACM Magazine for Students, 23(3), 32–35.
  • Dolgun, U. (2005). Enformasyon toplumundan gözetim toplumuna 21. yüzyılda gözetim, toplumsal denetim ve iktidar ilişkileri. Ekin Kitabevi.
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Toplam 98 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İletişim Çalışmaları
Bölüm Araştırma Makaleleri
Yazarlar

Elif Karakoç Keskin 0000-0002-2831-2247

Erken Görünüm Tarihi 28 Haziran 2024
Yayımlanma Tarihi 29 Haziran 2024
Gönderilme Tarihi 23 Ocak 2024
Kabul Tarihi 6 Haziran 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 16

Kaynak Göster

APA Karakoç Keskin, E. (2024). Dijital Medya Ortamında Rızanın Algoritmik İnşası: Eleştirel Algoritma Çalışmaları Perspektifinde Bir Tartışma. Yeni Medya(16), 329-353. https://doi.org/10.55609/yenimedya.1424585
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