MERİTOKRASİ TUZAĞININ DİNAMİKLERİ VE YAPAY ZEKÂ

Year 2024, Volume: 20 Issue: 3, 845 - 869

Mahmut Özer

https://doi.org/10.17130/ijmeb.1524229

Abstract

Bu çalışmada Daniel Markovitz’in Penguin Press tarafından yayınlanan “The Meritocracy
Trap: How America’s Foundational Myth Feeds Inequality, Dismantles the Middle Class, and Devours
the Elite” başlıklı kitabın meritokrasi tuzağının dinamikleri ve içerdiği iki farklı evre bağlamında
derinlemesine değerlendirmesi yapılmaktadır. Birinci evre, eğitim sisteminin ve işgücü piyasasının
herkese açıldığı bir döneme karşılık gelmektedir. Bu evrede beşeri sermayeye eğitim üzerinden eşit bir
şekilde yatırım yapılabilmektedir. Ancak, 1970’li yıllardan itibaren teknolojik dönüşümlerin otomasyonu
yaygınlaştırması, orta sınıfın bu avantajını ortadan kaldırmış, orta ve düşük becerili çalışanları işgücü
piyasasından ya uzaklaştırmış ya da daha düşük ücretlere mahkûm etmiştir. Orta sınıf otomasyonla
yerlerinde edilirken elit sınıf otomasyonun ortaya çıkardığı üst becerili ve dolayısıyla yüksek getirili işlerin sahibi olmaya başlamıştır. Bu dönüşümde eğitim sistemleri tekrar elitist yapısını ortaya çıkartarak
sadece varlıklı ailelerin güç yetirebildiği, işgücü piyasasında elit işlerle güçlü bağlara sahip yeni bir elit
bir eğitim seçeneği oluşturmuştur. Çalışmada ayrıca, yapay zekâ teknolojilerinin bu dinamikleri üçüncü
bir evreye taşıma potansiyeli tartışılmaktadır. Yapay zekâ teknolojilerinin yaygınlaşması, meritokratik
sistemi ikinci evreye geçiren dinamiklerin aynısına sahip olup otomasyonu çok daha güçlendirmektedir.
Bu nedenle bu çalışmada, yapay zekâ teknolojilerini sadece otomasyonu güçlendirme yerine istihdamı
merkeze alan ve işyerlerinde özellikle orta ve düşük becerili çalışanların becerilerini iyileştirerek insanı
tamamlayan ve toplam verimliliği yükselten bir yolda nasıl kullanılabileceği tartışılmaktadır.

Keywords

Meritokrasi, Eşitlik, Yapay zekâ, Eğitim, İşgücü piyasası

DYNAMICS OF THE MERITOCRACY TRAP AND ARTIFICIAL INTELLIGENCE

Abstract

In this study, Daniel Markovitz’s book titled “The Meritocracy Trap: How America’s Foundational
Myth Feeds Inequality, Dismantles the Middle Class, and Devours the Elite,” published by Penguin
Press, is thoroughly evaluated in the context of the dynamics of the meritocracy trap and the two
distinct phases it contains. The first phase corresponds to a period when the education system and the
labor market were open to everyone. During this phase, human capital could be equally invested in
through education. However, from the 1970s onwards, the spread of automation due to technological
transformations eliminated this advantage for the middle class, either displacing middle and low-skilled
workers from the labor market or condemning them to lower wages. While the middle class was replaced
by automation, the elite class began to own the high-skilled and consequently high-yielding jobs created
by automation. In this transformation, the education systems once again revealed their elitist structure,
creating a new elite educational option that was only accessible to wealthy families, strongly linked to
elite jobs in the labor market. The study also discusses the potential of artificial intelligence technologies
to move these dynamics into a third phase. The proliferation of artificial intelligence technologies has
dynamics similar to those that moved the meritocratic system into the second phase, greatly strengthening
automation. Therefore, this study discusses how artificial intelligence technologies can be used not merely
to strengthen automation but to focus on employment and enhance the skills of particularly middle and
low-skilled workers in workplaces, complementing human labor and increasing overall productivity.

Keywords

Meritocracy, Equality, Artificial intelligence, Education, Labor market

References

• Acemoglu, D., Autor, D., & Johnson, S. (2023). Can we have pro-worker AI? Choosing a path of machines in service of minds. MIT Shaping the Future of Work Initiative Policy Memo. Retrieved from https://shapingwork.mit.edu/wp-content/uploads/2023/09/Pro-Worker-AI-Policy-Memo.pdf.
• Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70, 9-49.Becker, G. S. (1964). Human capital: A theoretical and empirical analysis with special reference to education. New York: Columbia University Press.
• Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (No. w31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161.
• Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E.,…Viale, R. (2023).
• The impact of generative artificial intelligence on socioeconomic inequalities and policy making, PNAS Nexus, 3(6). https://doi.org/10.1093/pnasnexus/pgae191.
• Granovetter, M. S. (1974). Getting a job: A study of contacts and careers. Cambridge, MA: Harvard University Press.
• Green, A. (2019). What Is Happening to Middle Skill Workers? OECD Social, Employment and Migration Working Papers No. 230. Retrieved from https://www.oecd.org/content/dam/oecd/en/publications/ reports/2019/06/what-is-happening-to-middle-skill-workers_86bd2524/a934f8fa-en.pdf.
• İlikhan, S., Özer, M., Tanberkan, H., & Bozkurt, V. (2024). How to mitigate the risks of deployment of artificial intelligence in medicine? Turkish Journal of Medical Science, 54(3), 483-492. https:// doi.org/10.55730/1300-0144.5814.
• Jarvinen, T. (2020). Social background and labour market careers of young people: A comparison of two cohorts of Finnish young people not in employment, education or training (NEET). In K. Brunila & L. Lundahl (Eds.), Youth on the move: Tendencies and tensions in youth policies and practices (pp. 37-56). Helsinki, Finland: Helsinki University Press.
• Markovitz, D. (2019). The Meritocracy Trap: How America’s Foundational Myth Feeds Inequality, Dismantles the Middle Class, and Devours the Elite. New York: Penguin Press.
• Merton, R. K. (1968). The Matthew effect in science. Science, 159, 56-63. https://doi.org/10.1126/science. 159.3810.56.
• Montgomery, J. D. (1991). Social networks and labor-market outcomes: Towards and economic analysis. American Economic Review, 81(5), 1408-1418.
• Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586.
• OECD (2019). Under pressure: The squeezed middle class. Paris: OECD Publishing. https://doi. org/10.1787/689afed1-en.
• Özer, M. (2020). Okuldan İşe Geçiş Sorunlarında Bireyselci ve Yapısalcı Yaklaşım Çatışması. Bartın Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11(22), 330–345. https://doi.org/10.47129/ bartiniibf.759157.
• Özer, M., &Perc, M. (2021). Impact of social networks on the labor market inequalities and school-to-work transitions. Journal of Higher Education, 11(1), 38–50. https://doi.org/10.2399/yod.21.868353.
• Özer, M. (2022a). Türkiye’de eğitimi yeniden düşünmek. İstanbul: VakıfBank Kültür Yayınları.
• Özer, M. (2022b). The universalization of education in Türkiye and new orientations. İstanbul: TRT World Research Center.
• Özer, M. (2022c). Türkiye’de eğitimin evrenselleşmesi. İstanbul: Maltepe Üniversitesi Yayınları.
• Özer, M., & Perc, M. (2022). Improving equality in the education system of Türkiye. İstanbul University Journal of Sociology, 42(2), 325-334. https://doi.org/10.26650/SJ.2022.42.2.0035.
• Özer, M. (2023a). Matta etkisi. Uluslararası Yönetim İktisat ve İşletme Dergisi, 19(4), 974-984. https:// doi.org/10.17130/ijmeb.1374798.
• Özer, M. (2023b). The Matthew effect in Turkish education system. Bartın University Journal of Faculty of Education, 12(4), 704-712. https://doi.org/10.14686/buefad.1359312.
• Özer, M. (2023c). Impact of human capital and weak ties in social networks on employability. International Journal of Turkish Educational Studies, 11(21), 254-274. https://doi.org/10.46778/goputeb. 1351495.
• Özer, M. (2024a). Başarı oyununda Matta etkisi ve ödülün asimetrik dağılımı. Reflektif Journal of Social Sciences, 5(1), 187-197. https://doi.org/10.47613/reflektif.2024.153.
• Özer, M. (2024b). Potantial benefits and risks of artificial intelligence in education. Bartın University Journal of Faculty of Education, 13(2), 232-244. https://doi.org/10.14686/1416087.
• Özer, M., & Perc, M. (2024). Human complementation must aid automation to mitigate unemployment effects due to AI Technologies in the labor market. Reflektif Journal of Social Sciences, 5(2), 503- 514. https://doi.org/10.47613/reflektif.2024.176.
• Özer, M., Perc, M., & Suna, H. E. (2024a). Artificial intelligence bias and the amplification of inequalities in the labor market. Journal of Economy, Culture and Society, 69(1), 159-168. https://doi. org/10.26650/JECS2023-1415085.
• Özer, M., Perc, M., & Suna, H. E. (2024b). Participatory management can help AI ethics adhere to the social consensus. İstanbul University Journal of Sociology. https://doi.org/10.26650/SJ.2024.44.1.0001.
• Piketty, T. (2014). Capital in the twenty-first century. Cambridge, MA: Harvard University Press.
• Perc, M. (2014). The Matthew effect in emprical data. Journal of Royal Society Interface, 11(98). https:// doi.org/10.1098/rsif.2014.0378.
• Perc, M., Özer, M., & Hojnik, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5, 61. https://doi.org/10.1057/s41599-019-0278-x.
• Rigney, D. (2010). The matthew effect: How advantage begets further advantage. New York: Columbia University Press.
• Suna, H. E., Gür, B. S., Gelbal, S., ve Özer, M. (2020a). Fen lisesi öğrencilerinin sosyoekonomik arkaplanı ve yükseköğretime geçişteki tercihleri. Yükseköğretim Dergisi, 10(3), 356–370. https://doi. org/10.2399/yod.20.734921 .
• Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020b). Socioeconomic status and school type as predictors of academic achievement. Journal of Economy Culture and Society, 61, 41-64. https://doi.org/10.26650/JECS2020-0034.
• Suna, H. E., Özer, M., Şensoy, S., Gür, B. S., Gelbal, S., & Aşkar, P. (2021). Determinants of academic achievement in Turkey. Journal of Economy Culture and Society, 64, 143-162. https://doi. org/10.26650/JECS2021-934211.
• Suna, H. E. & Özer, M. (2022). The relationship of preschool attendance with academic achievement and socioeconomic status in Turkey. Journal of Measurement and Evaluation in Education and Psychology, 13(1), 54-68. https://doi.org/10.21031/epod.1060460.
• Suna, H.E., & Özer, M (2024). Medium- and long-term outcomes of early childhood education: experiences from Turkish large-scale assessments. Humanities & Social Sciences Communications, 11, 853. https://doi.org/10.1057/s41599-024-03241-9.
• United Nations Department of Economic and Social Affairs. (2020). Polarization of the labour market: are middle skills jobs disappearing? (Social Development Brief No. 10). United Nations. Retrieved from https://www.un.org/development/desa/dspd/wp-content/uploads/sites/22/2020/03/ SDBrief10-Polarization-of-the-Labour-Market.pdf.
• Zhang, T. (2024). The illusion of meritocracy. Social Science Information, 63(1), 114–128. https://doi. org/10.1177/05390184241230.
• Zuckerman, H. A. (1989). Accumulation of adavantage and disadvantage: The theory and its intellectual biography. In C. Mongardini, S. Tabboni (Eds.), Robert K. Merton and Contemporary Sociology (pp. 153-176). New Jersey: Transaction Publishers.