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The Societal Epistemology of Artificial Intelligence: The Rise of Algorithmic Authority in the Production of Knowledge in the Social Sciences

Year 2025, Volume: 14 Issue: 4, 144 - 160, 30.10.2025
https://doi.org/10.15869/itobiad.1730658

Abstract

Artificial intelligence and algorithms today are not limited to producing technical solutions; they are increasingly positioned as new epistemic apparatuses that regulate the processes of social knowledge production. This development has brought fundamental questions back into focus, particularly within the social sciences—questions about how knowledge is produced, by which normative criteria it is legitimized, and who gains epistemic authority in the process. This article aims to critically examine the transformative impact of AI technologies on the epistemology of the social sciences through the lens of a critical social epistemology, centered on the concept of algorithmic authority. The theoretical framework draws on Michel Foucault’s analysis of knowledge-power relations, Bruno Latour’s actor-network theory explaining the social construction of scientific knowledge, and Shoshana Zuboff’s theory of surveillance capitalism. These three approaches allow for an understanding of algorithmic systems not merely as technical operations but as structures that shape the normative and ideological dimensions of knowledge regimes. Contributions from thinkers such as Tarleton Gillespie and Nick Seaver, who conceptualize algorithms as cultural production tools, complement the article’s conceptual foundation. Methodologically, the study employs critical literature review and comparative theoretical analysis. The article explores the application of digital methodologies in the social sciences through case studies such as sentiment analysis, social media research, and ethnographic text processing. It highlights key issues that emerge in these processes, including algorithmic bias, methodological reductionism, and lack of transparency. In conclusion, the role of AI in social knowledge production emerges as not only a technical issue but also a political and normative one.

References

  • Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645.
  • Beer, D. (2016). Metric power. Palgrave Macmillan.
  • Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922.
  • Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Press.
  • Boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878.
  • Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
  • Crawford, K. (2021). The Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
  • Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G.
  • Diakopoulos, N. (2015). Algorithmic accountability: Journalistic investigation of computational power structures. Digital Journalism, 3(3), 398–415. https://doi.org/10.1080/21670811.2014.976411.
  • Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  • Flick, U. (2014). An introduction to qualitative research (5th ed.). SAGE Publications.
  • Flyvbjerg, B. (2001). Making social science matter: Why social inquiry fails and how it can succeed again. Cambridge University Press.
  • Foucault, M. (1977). Discipline and punish: The birth of the prison (A. M. Sheridan, Trans.). Vintage Books.
  • Foucault, M. (1980). Power/knowledge: Selected interviews and other writings, 1972–1977 (C. Gordon, Ed.). Pantheon Books.
  • Fricker, M. (2022). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.
  • Geertz, C. (1973). The interpretation of cultures: Selected essays. Basic Books.
  • Gerring, J. (2001). Social science methodology: A criterial framework. Cambridge University Press.
  • Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media technologies: Essays on communication, materiality, and society (pp. 167–194). MIT Press.
  • Given, L. M. (2008). The SAGE encyclopedia of qualitative research methods. SAGE Publications.
  • Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599.
  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications.
  • Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Harvard University Press.
  • Latour, B. (1999). Pandora’s hope: Essays on the reality of science studies. Harvard University Press.
  • Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientific facts (2nd ed.). Princeton University Press.
  • Lyon, D. (2006). Surveillance studies: An overview. Polity Press.
  • Marres, N. (2017). Digital sociology: The reinvention of social research. Polity Press.
  • Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4.
  • O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Penguin Books.
  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
  • Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
  • Rieder, B. (2020). Engines of order: A mechanology of algorithmic techniques. Amsterdam University Press.
  • Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885–899. https://doi.org/10.1177/0038038507080443.
  • Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 1–12. https://doi.org/10.1177/2053951717738104.
  • Weber, M. (1949). The methodology of the social sciences (E. A. Shils & H. A. Finch, Trans.). Free Press. (Original work published 1904–1917).
  • Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.

Yapay Zekânın Toplumsal Epistemolojisi: Sosyal Bilimlerde Bilgi Üretiminde Algoritmik Otoritenin Yükselişi

Year 2025, Volume: 14 Issue: 4, 144 - 160, 30.10.2025
https://doi.org/10.15869/itobiad.1730658

Abstract

Yapay zekâ ve algoritmalar, günümüzde yalnızca teknik çözümler üretmekle sınırlı kalmamakta; aynı zamanda toplumsal bilgi üretim süreçlerini düzenleyen yeni epistemik aygıtlar olarak konumlanmaktadır. Bu durum, özellikle sosyal bilimlerde, bilginin nasıl üretildiği, hangi normatif ölçütlerle meşrulaştırıldığı ve bu süreçte kimin otorite kazandığı gibi temel soruların yeniden gündeme gelmesine yol açmaktadır. Bu makale, algoritmik otorite kavramı ekseninde, yapay zekâ teknolojilerinin sosyal bilim epistemolojisi üzerindeki dönüştürücü etkisini eleştirel bir toplumsal epistemoloji çerçevesinden incelemeyi amaçlamaktadır. Kuramsal temelde Michel Foucault’nun bilgi-iktidar ilişkilerine dair yaklaşımı, Bruno Latour’un bilimsel bilginin toplumsal inşasını açıklayan aktör-ağ kuramı ve Shoshana Zuboff’un gözetim kapitalizmi çerçevesi esas alınmıştır. Bu üç yaklaşım, algoritmik sistemlerin yalnızca teknik işleyişine değil, aynı zamanda bilgi rejimlerinin normatif ve ideolojik yapısına etkide bulunan unsurlar olarak değerlendirilmelerine olanak tanımaktadır. Ayrıca Tarleton Gillespie ve Nick Seaver gibi düşünürlerin algoritmaların kültürel üretim araçları olduğu yönündeki katkıları, çalışmanın kavramsal zemininin tamamlayıcı boyutunu oluşturmaktadır. Yöntem olarak eleştirel literatür taraması ve kuramlar arası karşılaştırmalı çözümleme kullanılmıştır. Makale, dijital metodolojilerin sosyal bilimlerdeki uygulama biçimlerini duygu analizi, sosyal medya araştırmaları ve etnografik metin işleme gibi örneklerle tartışmakta; bu süreçlerde ortaya çıkan algoritmik önyargı, metodolojik indirgemecilik ve şeffaflık eksikliği gibi sorunları ortaya koymaktadır. Sonuç olarak, yapay zekânın toplumsal bilgi üretimindeki rolü teknik olduğu kadar politik ve normatif bir meseleye dönüşmüştür.

References

  • Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645.
  • Beer, D. (2016). Metric power. Palgrave Macmillan.
  • Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922.
  • Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Press.
  • Boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878.
  • Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
  • Crawford, K. (2021). The Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
  • Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G.
  • Diakopoulos, N. (2015). Algorithmic accountability: Journalistic investigation of computational power structures. Digital Journalism, 3(3), 398–415. https://doi.org/10.1080/21670811.2014.976411.
  • Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  • Flick, U. (2014). An introduction to qualitative research (5th ed.). SAGE Publications.
  • Flyvbjerg, B. (2001). Making social science matter: Why social inquiry fails and how it can succeed again. Cambridge University Press.
  • Foucault, M. (1977). Discipline and punish: The birth of the prison (A. M. Sheridan, Trans.). Vintage Books.
  • Foucault, M. (1980). Power/knowledge: Selected interviews and other writings, 1972–1977 (C. Gordon, Ed.). Pantheon Books.
  • Fricker, M. (2022). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.
  • Geertz, C. (1973). The interpretation of cultures: Selected essays. Basic Books.
  • Gerring, J. (2001). Social science methodology: A criterial framework. Cambridge University Press.
  • Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Media technologies: Essays on communication, materiality, and society (pp. 167–194). MIT Press.
  • Given, L. M. (2008). The SAGE encyclopedia of qualitative research methods. SAGE Publications.
  • Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599.
  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications.
  • Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Harvard University Press.
  • Latour, B. (1999). Pandora’s hope: Essays on the reality of science studies. Harvard University Press.
  • Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientific facts (2nd ed.). Princeton University Press.
  • Lyon, D. (2006). Surveillance studies: An overview. Polity Press.
  • Marres, N. (2017). Digital sociology: The reinvention of social research. Polity Press.
  • Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s42256-019-0114-4.
  • O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Penguin Books.
  • Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
  • Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
  • Rieder, B. (2020). Engines of order: A mechanology of algorithmic techniques. Amsterdam University Press.
  • Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41(5), 885–899. https://doi.org/10.1177/0038038507080443.
  • Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 1–12. https://doi.org/10.1177/2053951717738104.
  • Weber, M. (1949). The methodology of the social sciences (E. A. Shils & H. A. Finch, Trans.). Free Press. (Original work published 1904–1917).
  • Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
There are 35 citations in total.

Details

Primary Language English
Subjects Sociology and Social Studies of Science and Technology
Journal Section Research Article
Authors

Melek Coşgun Solak 0000-0001-6249-5043

Submission Date June 30, 2025
Acceptance Date October 27, 2025
Early Pub Date October 27, 2025
Publication Date October 30, 2025
Published in Issue Year 2025 Volume: 14 Issue: 4

Cite

APA Coşgun Solak, M. (2025). The Societal Epistemology of Artificial Intelligence: The Rise of Algorithmic Authority in the Production of Knowledge in the Social Sciences. İnsan Ve Toplum Bilimleri Araştırmaları Dergisi, 14(4), 144-160. https://doi.org/10.15869/itobiad.1730658

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