Öz
Yapay zekanın (YZ) gelişimi, açıklama ve etiketleme için kapsamlı insan emeği gerektiren geniş veri kümelerine büyük ölçüde dayanmaktadır. YZ'nin özerk ve akıllı bir sistem olarak tasvir edilmesine rağmen, işlevselliği özellikle Küresel Güney'de güvencesiz dijital emeğe derinden bağımlıdır. YZ eğitiminde kritik bir süreç olan veri etiketleme, genellikle dış kaynaklı veya kalabalık kaynaklıdır ve çalışanları düşük ücretlere, iş güvencesizliğine ve sömürücü çalışma koşullarına maruz bırakmaktadır. Bu çalışma, YZ, emek ve küresel eşitsizliklerin kesişimini inceleyerek, Dijital Taylorizm ve verileştirmenin işçi gözetimini nasıl yoğunlaştırdığını ve insan emeğini parçalanmış, tekrarlayan görevlere nasıl indirgediğini vurgulamaktadır. Dijital terzihanelerin ve dijital bir alt sınıfın yükselişi, YZ gelişiminin Küresel Kuzey ve Güney arasındaki tarihi ekonomik bağımlılık modellerini nasıl sürdürdüğünü daha da göstermektedir. Kitle kaynaklı platformların ve dış kaynaklı emek piyasalarının analizi yoluyla, bu araştırma YZ'nin salt teknolojik bir ilerleme olduğu yönündeki baskın söyleme meydan okuyarak sosyoekonomik etkilerini ortaya koymaktadır. Bulgular, etik YZ geliştirme, işgücü uygulamalarında daha fazla şeffaflık ve işçileri sömürüden korumak için yapısal reformlara olan ihtiyacı vurgulamaktadır. Müdahale olmadan, YZ küresel eşitsizlikleri derinleştirme, serveti ve gücü yoğunlaştırma ve dijital ekonomideki sistemsel eşitsizlikleri güçlendirme riski taşımaktadır. Anahtar Kelimeler: Yapay Zeka, Dijital Emek, Kitle Kaynak, Dijital Taylorizm, Küresel Eşitsizlik, Verileştirme, Prekarya Emeği
Anahtar Kelimeler
Yapay Zeka Dijital Emek Kitle Kaynak Dijital Taylorizm Küresel Eşitsizlik Verileştirme Prekarya Emeği
Kaynakça
- Acemoğlu, D., & Johnson, S. (2023). İktidar ve teknoloji: Bin yıllık mücadele (C. Duran, Trans.). Doğan Kitap.
- Altenried, M. (2022). The digital factory: The human labor of automation. University of Chicago Press. https://doi.org/10.7208/chicago/9780226815503.001.0001
- Aydoğan, F. (2019). Tekno-metalaşan ve “emeğe” dönüşen oyun. In F. Aydoğan (Ed.), Endüstri 4.0 ve dijital medya (pp. 85–102). Der.
- Bekar, N. (2021). Küresel güvenlik mi, güvenliğin küreselleşmesi mi?: Yirmi birinci yüzyılın güvenlik kavramı üzerine bir değerlendirme. Nika.
- Berardi, F. B. (2012). Ruh işbaşında (F. Genç, Trans.). Metis.
- Brown, P., Lauder, H., & Ashton, D. (2011). The global auction: The broken promises of education, jobs and incomes. Oxford University Press.
- Burawoy, M. (2015). Üretim siyaseti: Kapitalizm ve sosyalizmde fabrika rejimleri (Ç. Gümüşoluk, Trans.). NotaBene.
- Chandran, R., Smith, A., & Ramos, M. (2023, July 6). AI boom is dream and nightmare for workers in Global South. Context. Retrieved from https://www.context.news/ai/ai-boom-is-dream-and-nightmare-for-workers-in-global-south
- Cheng, M. (2023, October 10). Microsoft, Google, and OpenAI are getting questioned about their AI "data labellers". Quartz. Retrieved from https://qz.com/tech-companies-ai-data-labelers-congress-1850834407
- Couldry, N., & Mejias, U. A. (2023). The decolonial turn in data and technology research: What is at stake and where is it heading? Information, Communication & Society, 26(4), 786–802. https://doi.org/10.1080/1369118X.2021.1986102
- Crawford, K., & Paglen, T. (2021). Excavating AI: The politics of images in machine learning training sets. AI & Society, 36(4), 1105–1116. https://doi.org/10.1007/s00146-021-01192-2
- DeWinter, J., Kocurek, C. A., & Nichols, R. (2014). Taylorism 2.0: Gamification, scientific management and the capitalist appropriation of play. Journal of Gaming & Virtual Worlds, 6(2), 109–127.
- Doğan, M. (2021). Güçlü yapay zeka mümkün müdür? Pasajlar Sosyal Bilimler Dergisi, 9, 85–102. Enormis.
- Finn, E. (2020). Algoritmalar ne ister? Hesaplama çağında hayal gücü (S. Köse, Çev.). Tellekt.
- Goetze, T. S. (2024). AI art is theft: Labour, extraction, and exploitation: Or, on the dangers of stochastic Pollocks. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 186–196).
- Gray, M. L., & Suri, S. (2019). Ghost work: How to stop Silicon Valley from building a new global underclass. Harper Business.
- Haskins, C. (2024). The low-paid humans behind AI’s smarts ask Biden to free them from “modern day slavery”. WIRED. Retrieved from https://www.wired.com/story/low-paid-humans-ai-biden-modern-day-slavery
- Huws, U. (2015). Dijital çağda sınıfın payandaları: Yaşama, emek ve değer. In L. Panitch, G. Albo, & V. Chibber (Eds.), Socialist Register 2014: 21. yüzyılda sınıflar ve sınıf mücadelesi (T. Öncel, Trans.). Yordam.
- Irani, L. (2019). Justice for data janitors. In S. M. Brouillette (Ed.), Think in public: A Public Books reader (pp. 23–40). Columbia University Press.
- Khanna, S., & Chandran, R. (2023, August 14). Gigs, scams, ghost work: India tech sector’s dark side. Context. Retrieved from https://www.context.news/big-tech/gigs-scams-ghost-work-india-tech-sectors-dark-side
- Le Ludec, C., & Cornet, M. (2023, June 5). How low-paid workers in Madagascar power French tech’s AI ambitions. The Conversation. Retrieved from https://theconversation.com/how-low-paid-workers-in-madagascar-power-french-techs-ai-ambitions-202421
- Le Ludec, C., Cornet, M., & Casilli, A. A. (2023). The problem with annotation: Human labour and outsourcing between France and Madagascar. Big Data & Society, 10(2), 20539517231188723.
- Li, K., Li, G., Wang, Y., Huang, Y., Liu, Z., & Wu, Z. (2021). CrowdRL: An end-to-end reinforcement learning framework for data labelling. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 289–300). IEEE.
- McCullen, A. (2024). Technological taylorism: How modern AI is reshaping the future of work. The Innovation Show. Retrieved from https://theinnovationshow.io/episode/technological-taylorism-how-modern-ai-is-reshaping-the-future-of-work/
- McQuillan, D. (2024). Yapay zekaya direnmek: Antifaşist bir yaklaşım (D. Saraçoğlu, Trans.). NotaBene.
- Mejias, U., & Couldry, N. (2024). Data grab: The new colonialism of Big Tech and how to fight back. University of Chicago Press. https://doi.org/10.7208/chicago/9780226832319
- Morreale, F., Bahmanteymouri, E., Burmester, B., Chen, A., & Thorp, M. (2024). The unwitting labourer: Extracting humanness in AI training. AI & Society, 39(5), 2389–2399.
- Muldoon, J., Graham, M., & Cant, C. (2024, July 6). Meet Mercy and Anita- the African workers driving the AI revolution, for just over a dollar an hour. The Guardian. Retrieved from https://www.theguardian.com/technology/article/2024/jul/06/mercy-anita-african-workers-ai-artificial-intelligence-exploitation-feeding-machine
- Nyholm, S. (2024). Bir robot (iyi) bir iş arkadaşı olabilir mi? (M. Filimowicz, Ed.; A. L. Orcan, Trans.). The Kitap.
- Pehlivanlı, E. A. (2021). Yapay zeka etiği. In S. Y. Kandır & B. Nakipoğlu (Eds.), Güncel işletme yönetimi çalışmaları (pp. 145–160). Akademisyen.
- Pogrebna, G. (2024, October 8). AI is a multi-billion dollar industry. It’s underpinned by an invisible and exploited workforce. The Conversation. Retrieved from https://theconversation.com/ai-is-a-multi-billion-dollar-industry-its-underpinned-by-an-invisible-and-exploited-workforce-216710
- Reese, B. (2020). Yapay zeka çağı (M. Doğan, Trans.). Say.
- Regilme, S. S. F. (2024). Artificial intelligence colonialism: Environmental damage, labor exploitation, and human rights crises in the Global South. SAIS Review of International Affairs, 44(2), 75–92.
- Rendueles, C. (2024). Sosyofobi: Dijital ütopya çağında siyasal değişim (A. Türker Ok, Trans.). İletişim.
- Rogers, B. (2023). Data and democracy at work: Advanced information technologies, labor law, and the new working class. The MIT Press.
- Samancı, B. (2024, February 10). Data labelling for machine learning. Medium. Retrieved from https://medium.com/@betulsamancii/what-is-data-labeling-how-to-do-it-05ce22c10b76
- Scannell, P. (2020). Medya ve iletişim (O. Taş & B. Sümer, Trans.). Ütopya.
- Taylor, B. L. (2023, October 3). Long hours and low wages: The human labour powering AI’s development. The Conversation. Retrieved from https://theconversation.com/long-hours-and-low-wages-the-human-labour-powering-ais-development-217038
- Vijeyarasa, R. (2014). Hidden data, hidden victims: Trafficking in the context of globalisation and labour exploitation-The case of Vietnam. In Labour and global justice: Essays on ethics of labour practices under globalisation.
- Williams, A., Miceli, M., & Gebru, T. (2022). The exploited labour behind artificial intelligence. Noema Magazine. Retrieved from https://www.noemamag.com/the-exploited-labor-behind-artificial-intelligence/
- Xiang, C. (2023, December 8). OpenAI used Kenyan workers making $2 an hour to filter traumatic content from ChatGPT. VICE. Retrieved from https://www.vice.com/en/article/openai-used-kenyan-workers-making-dollar2-an-hour-to-filter-traumatic-content-from-chatgpt
- Yılmaz, Ö. (2024). Dijital kapitalizmde eşitsizliğin dijital emek bağlamında yeniden üretimi: Afrika kıtası örneği [Doctoral thesis, İstanbul University Institute of Social Sciences].
- Zhang, J., Wu, X., & Sheng, V. S. (2016). Learning from crowdsourced labelled data: A survey. Artificial Intelligence Review, 46, 543–576.
- Zhou, V., & Chen, C. (2023, September 15). China’s AI boom depends on an army of exploited student interns. Rest of World. Retrieved from https://restofworld.org/2023/china-ai-student-labor
Labour And Exploitation Processes in Artificial Intelligence: Example of Digital Taylorism in Data Labelling
Öz
The development of artificial intelligence (AI) heavily relies on vast datasets that require extensive human labour for annotation and labelling. Despite AI's portrayal as an autonomous and intelligent system, its functionality is deeply dependent on precarious digital labour, particularly in the Global South. Data labelling, a critical process in AI training, is often outsourced or crowdsourced, subjecting workers to low wages, job insecurity, and exploitative working conditions. This study examines the intersection of AI, labour, and global inequalities, highlighting how Digital Taylorism and datafication intensify worker surveillance and reduce human labour to fragmented, repetitive tasks. The rise of digital sweatshops and a digital underclass further illustrates how AI development perpetuates historical patterns of economic dependency between the Global North and South. Through an analysis of crowdsourcing platforms and outsourced labour markets, this research challenges the dominant discourse of AI as a purely technological advancement, revealing its socio-economic implications. The findings emphasize the need for ethical AI development, greater transparency in labour practices, and structural reforms to protect workers from exploitation. Without intervention, AI risks deepening global disparities, concentrating wealth and power while reinforcing systemic inequalities in the digital economy.
Anahtar Kelimeler
Artificial Intelligence Digital Labor Crowdsourcing Digital Taylorism Global Inequality Datafication Precarious Work
Kaynakça
- Acemoğlu, D., & Johnson, S. (2023). İktidar ve teknoloji: Bin yıllık mücadele (C. Duran, Trans.). Doğan Kitap.
- Altenried, M. (2022). The digital factory: The human labor of automation. University of Chicago Press. https://doi.org/10.7208/chicago/9780226815503.001.0001
- Aydoğan, F. (2019). Tekno-metalaşan ve “emeğe” dönüşen oyun. In F. Aydoğan (Ed.), Endüstri 4.0 ve dijital medya (pp. 85–102). Der.
- Bekar, N. (2021). Küresel güvenlik mi, güvenliğin küreselleşmesi mi?: Yirmi birinci yüzyılın güvenlik kavramı üzerine bir değerlendirme. Nika.
- Berardi, F. B. (2012). Ruh işbaşında (F. Genç, Trans.). Metis.
- Brown, P., Lauder, H., & Ashton, D. (2011). The global auction: The broken promises of education, jobs and incomes. Oxford University Press.
- Burawoy, M. (2015). Üretim siyaseti: Kapitalizm ve sosyalizmde fabrika rejimleri (Ç. Gümüşoluk, Trans.). NotaBene.
- Chandran, R., Smith, A., & Ramos, M. (2023, July 6). AI boom is dream and nightmare for workers in Global South. Context. Retrieved from https://www.context.news/ai/ai-boom-is-dream-and-nightmare-for-workers-in-global-south
- Cheng, M. (2023, October 10). Microsoft, Google, and OpenAI are getting questioned about their AI "data labellers". Quartz. Retrieved from https://qz.com/tech-companies-ai-data-labelers-congress-1850834407
- Couldry, N., & Mejias, U. A. (2023). The decolonial turn in data and technology research: What is at stake and where is it heading? Information, Communication & Society, 26(4), 786–802. https://doi.org/10.1080/1369118X.2021.1986102
- Crawford, K., & Paglen, T. (2021). Excavating AI: The politics of images in machine learning training sets. AI & Society, 36(4), 1105–1116. https://doi.org/10.1007/s00146-021-01192-2
- DeWinter, J., Kocurek, C. A., & Nichols, R. (2014). Taylorism 2.0: Gamification, scientific management and the capitalist appropriation of play. Journal of Gaming & Virtual Worlds, 6(2), 109–127.
- Doğan, M. (2021). Güçlü yapay zeka mümkün müdür? Pasajlar Sosyal Bilimler Dergisi, 9, 85–102. Enormis.
- Finn, E. (2020). Algoritmalar ne ister? Hesaplama çağında hayal gücü (S. Köse, Çev.). Tellekt.
- Goetze, T. S. (2024). AI art is theft: Labour, extraction, and exploitation: Or, on the dangers of stochastic Pollocks. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 186–196).
- Gray, M. L., & Suri, S. (2019). Ghost work: How to stop Silicon Valley from building a new global underclass. Harper Business.
- Haskins, C. (2024). The low-paid humans behind AI’s smarts ask Biden to free them from “modern day slavery”. WIRED. Retrieved from https://www.wired.com/story/low-paid-humans-ai-biden-modern-day-slavery
- Huws, U. (2015). Dijital çağda sınıfın payandaları: Yaşama, emek ve değer. In L. Panitch, G. Albo, & V. Chibber (Eds.), Socialist Register 2014: 21. yüzyılda sınıflar ve sınıf mücadelesi (T. Öncel, Trans.). Yordam.
- Irani, L. (2019). Justice for data janitors. In S. M. Brouillette (Ed.), Think in public: A Public Books reader (pp. 23–40). Columbia University Press.
- Khanna, S., & Chandran, R. (2023, August 14). Gigs, scams, ghost work: India tech sector’s dark side. Context. Retrieved from https://www.context.news/big-tech/gigs-scams-ghost-work-india-tech-sectors-dark-side
- Le Ludec, C., & Cornet, M. (2023, June 5). How low-paid workers in Madagascar power French tech’s AI ambitions. The Conversation. Retrieved from https://theconversation.com/how-low-paid-workers-in-madagascar-power-french-techs-ai-ambitions-202421
- Le Ludec, C., Cornet, M., & Casilli, A. A. (2023). The problem with annotation: Human labour and outsourcing between France and Madagascar. Big Data & Society, 10(2), 20539517231188723.
- Li, K., Li, G., Wang, Y., Huang, Y., Liu, Z., & Wu, Z. (2021). CrowdRL: An end-to-end reinforcement learning framework for data labelling. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 289–300). IEEE.
- McCullen, A. (2024). Technological taylorism: How modern AI is reshaping the future of work. The Innovation Show. Retrieved from https://theinnovationshow.io/episode/technological-taylorism-how-modern-ai-is-reshaping-the-future-of-work/
- McQuillan, D. (2024). Yapay zekaya direnmek: Antifaşist bir yaklaşım (D. Saraçoğlu, Trans.). NotaBene.
- Mejias, U., & Couldry, N. (2024). Data grab: The new colonialism of Big Tech and how to fight back. University of Chicago Press. https://doi.org/10.7208/chicago/9780226832319
- Morreale, F., Bahmanteymouri, E., Burmester, B., Chen, A., & Thorp, M. (2024). The unwitting labourer: Extracting humanness in AI training. AI & Society, 39(5), 2389–2399.
- Muldoon, J., Graham, M., & Cant, C. (2024, July 6). Meet Mercy and Anita- the African workers driving the AI revolution, for just over a dollar an hour. The Guardian. Retrieved from https://www.theguardian.com/technology/article/2024/jul/06/mercy-anita-african-workers-ai-artificial-intelligence-exploitation-feeding-machine
- Nyholm, S. (2024). Bir robot (iyi) bir iş arkadaşı olabilir mi? (M. Filimowicz, Ed.; A. L. Orcan, Trans.). The Kitap.
- Pehlivanlı, E. A. (2021). Yapay zeka etiği. In S. Y. Kandır & B. Nakipoğlu (Eds.), Güncel işletme yönetimi çalışmaları (pp. 145–160). Akademisyen.
- Pogrebna, G. (2024, October 8). AI is a multi-billion dollar industry. It’s underpinned by an invisible and exploited workforce. The Conversation. Retrieved from https://theconversation.com/ai-is-a-multi-billion-dollar-industry-its-underpinned-by-an-invisible-and-exploited-workforce-216710
- Reese, B. (2020). Yapay zeka çağı (M. Doğan, Trans.). Say.
- Regilme, S. S. F. (2024). Artificial intelligence colonialism: Environmental damage, labor exploitation, and human rights crises in the Global South. SAIS Review of International Affairs, 44(2), 75–92.
- Rendueles, C. (2024). Sosyofobi: Dijital ütopya çağında siyasal değişim (A. Türker Ok, Trans.). İletişim.
- Rogers, B. (2023). Data and democracy at work: Advanced information technologies, labor law, and the new working class. The MIT Press.
- Samancı, B. (2024, February 10). Data labelling for machine learning. Medium. Retrieved from https://medium.com/@betulsamancii/what-is-data-labeling-how-to-do-it-05ce22c10b76
- Scannell, P. (2020). Medya ve iletişim (O. Taş & B. Sümer, Trans.). Ütopya.
- Taylor, B. L. (2023, October 3). Long hours and low wages: The human labour powering AI’s development. The Conversation. Retrieved from https://theconversation.com/long-hours-and-low-wages-the-human-labour-powering-ais-development-217038
- Vijeyarasa, R. (2014). Hidden data, hidden victims: Trafficking in the context of globalisation and labour exploitation-The case of Vietnam. In Labour and global justice: Essays on ethics of labour practices under globalisation.
- Williams, A., Miceli, M., & Gebru, T. (2022). The exploited labour behind artificial intelligence. Noema Magazine. Retrieved from https://www.noemamag.com/the-exploited-labor-behind-artificial-intelligence/
- Xiang, C. (2023, December 8). OpenAI used Kenyan workers making $2 an hour to filter traumatic content from ChatGPT. VICE. Retrieved from https://www.vice.com/en/article/openai-used-kenyan-workers-making-dollar2-an-hour-to-filter-traumatic-content-from-chatgpt
- Yılmaz, Ö. (2024). Dijital kapitalizmde eşitsizliğin dijital emek bağlamında yeniden üretimi: Afrika kıtası örneği [Doctoral thesis, İstanbul University Institute of Social Sciences].
- Zhang, J., Wu, X., & Sheng, V. S. (2016). Learning from crowdsourced labelled data: A survey. Artificial Intelligence Review, 46, 543–576.
- Zhou, V., & Chen, C. (2023, September 15). China’s AI boom depends on an army of exploited student interns. Rest of World. Retrieved from https://restofworld.org/2023/china-ai-student-labor
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Bilim ve Teknoloji Sosyolojisi ve Sosyal Bilimler, İletişim Sosyolojisi |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Haziran 2025 |
| Gönderilme Tarihi | 25 Şubat 2025 |
| Kabul Tarihi | 15 Mayıs 2025 |
| Yayımlandığı Sayı | Yıl 2025 Sayı: 15 |
