Artificial Intelligence Agents, Capital, and Symbolic Violence: Toward a Field Theory of AI in the Social Sciences

Authors

Keywords:

cultural capital, field theory, habitus, Pierre Bourdieu, symbolic violence, artificial intelligence

Abstract

AI agents intervene in decisions about work, schooling, credit and political communication, and they do so without appearing as participants at all. This article reads them through Pierre Bourdieu's conceptual apparatus. The argument is that these systems redistribute capital across the fields where they operate, and that they draw their authority from the very procedure that keeps their classificatory principle out of reach. Three lines of analysis are developed: AI agents as a form of institutionalized cultural capital that conceals its social origin; the uneven restructuring of the educational, financial, and political fields; and the technical habitus of developers as a source of symbolic violence. The article closes with a research program organized around the notion of the field theory of AI.

Downloads

Download data is not yet available.

Author Biography

  • Carlos De Angelis, University of Buenos Aires

    Carlos Fernando De Angelis is a sociologist, researcher, and university lecturer at the Faculty of Social Sciences, University of Buenos Aires (UBA), Argentina. His research lies at the intersection of social theory, the sociology of time, public opinion studies, and the social implications of artificial intelligence and digital technologies.

    He has completed his doctoral research in Social Sciences at the University of Buenos Aires. His dissertation examines the politics of time under contemporary capitalism, focusing on social acceleration, desynchronization, and conflicts over social temporality. Drawing on critical theory, classical and contemporary sociology, and Latin American intellectual traditions, his work investigates the relationships between technology, power, subjectivity, and social change.

    His academic career combines theoretical inquiry, methodological research, and empirical studies. He has published extensively on public opinion, political behavior, digital culture, platform societies, and social research methods. He is the author of Nueva Opinión Pública (2021) and Radiografía del voto porteño (2025), as well as numerous peer reviewed articles, book chapters, and conference papers presented at international academic meetings.

    At the University of Buenos Aires, he has taught courses in public opinion and social research methodology for more than two decades. His teaching is closely linked to research training, quantitative and qualitative methods, and the analysis of political and social transformations.

    His current research agenda develops a critical sociological perspective on artificial intelligence, examining its consequences for temporality, everyday life, knowledge production, and contemporary forms of social organization. Alongside his academic work, he advises public and private organizations on the social impact of artificial intelligence and the transformation of productive processes through emerging technologies.

References

[1] Noble, S. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

[2] Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.

[3] Bourdieu, P. (1986b). The forms of capital. In Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood Press.

[4] Bourdieu, P. (1996). The rules of art: Genesis and structure of the literary field. Stanford University Press.

[5] Bourdieu, P., & Passeron, J.-C. (1977). Reproduction in education, society and culture. SAGE.

[6] Kretschmann, A. (2025). On Pierre Bourdieu’s legal thought: Toward a classic of socio-legal studies. Annual Review of Law and Social Science, 21(1), 17–34. https://doi.org/10.1146/annurev-lawsocsci-062124-122138

[7] Verwiebe, R., & Hagemann, S. (2025). Bourdieu revisited: New forms of digital capital – emergence, reproduction, inequality of distribution. Information, Communication & Society, 28(11), 1861–1883. https://doi.org/10.1080/1369118X.2024.2358170

[8] Harker, R., & May, S. A. (1993). Code and habitus: Comparing the accounts of Bernstein and Bourdieu. British Journal of Sociology of Education, 14(2), 169–178. https://doi.org/10.1080/0142569930140204

[9] Romele, A. (2023). Digital habitus: A critique of the imaginaries of artificial intelligence (1st ed.). Routledge. https://doi.org/10.4324/9781003400479

[10] Bourdieu, P. (1977a). Outline of a theory of practice. Cambridge University Press.

[11] Bourdieu, P., & Wacquant, L. J. D. (1992). An invitation to reflexive sociology. University of Chicago Press.

[12] Bourdieu, P. (1977b). Outline of a theory of practice. Cambridge University Press.

[13] Bourdieu, P. (2001). Masculine domination. Polity Press.

[14] Bourdieu, P. (1986a). Habitus, code et codification. Actes de la recherche en sciences sociales, 64(1), 40–44. https://doi.org/10.3406/arss.1986.2335

[15] Hargittai, E. (2002). Second-level digital divide: Differences in people’s online skills. First Monday, 7(4). https://doi.org/10.5210/fm.v7i4.942

[16] Ragnedda, M., & Muschert, G. W. (2013). The digital divide: The internet and social inequality in international perspective. Routledge.

[17] Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.

[18] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.

[19] Avnoon, N., & Eyal, G. (2026). It’s not a bug, it’s a feature: How AI experts and data scientists account for the opacity of algorithms. Social Studies of Science, 56(1), 28–52. https://doi.org/10.1177/03063127251364509

[20] Romele, A., & Rodighiero, D. (2020). Digital habitus or personalization without personality. https://doi.org/10.5281/zenodo.3950031

[21] Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512. https://doi.org/10.1177/2053951715622512

[22] Bucher, T. (2018). If…then: Algorithmic power and politics. Oxford University Press.

[23] De Angelis, C. F. (2026). The rise of algorithmic society: History, power and subjectivity in digital capitalism. Generis Publishing.

[24] Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

[25] West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems: Gender, race and power in AI. AI Now Institute.

[26] Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste. Harvard University Press.

[27] O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.

[28] Bourdieu, P. (1974). Avenir de classe et causalité du probable. Revue française de sociologie, 15(1), 3–42. https://doi.org/10.2307/3320261

[29] Martin, K., & Waldman, A. (2023). Are algorithmic decisions legitimate? The effect of process and outcomes on perceptions of legitimacy of AI decisions. Journal of Business Ethics, 183(3), 653–670. https://doi.org/10.1007/s10551-021-05032-7

[30] Reich, J. (2020). Failure to disrupt: Why technology alone can’t transform education. Harvard University Press.

[31] Fourcade, M., & Healy, K. (2013). Classification situations: Life-chances in the neoliberal era. Accounting, Organizations and Society, 38(8), 559–572. https://doi.org/10.1016/j.aos.2013.11.002

[32] Persily, N., & Tucker, J. A. (2020). Social media and democracy: The state of the field, prospects for reform. Cambridge University Press.

[33] Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7). https://doi.org/10.5210/fm.v19i7.4901

[34] Kreiss, D. (2016). Prototype politics: Technology-intensive campaigning and the data of democracy. Oxford University Press.

[35] Broussard, M. (2018). Artificial unintelligence: How computers misunderstand the world. MIT Press.

[36] Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica.

[37] Katzenbach, C., & Ulbricht, L. (2019). Algorithmic governance. Internet Policy Review, 8(4). https://doi.org/10.14763/2019.4.1424

[38] Pandey, J. K., & Kumar, R. (2025). Governing the algorithmic agent: Confronting overt and covert challenges to justice and the future of work. International Journal of Law Management & Humanities, 8(4), 1974–1984. https://doij.org/10.10000/IJLMH.1110648

[39] Lalitha Kumari, P., & Pandey, J. K. (2023). Impact of artificial intelligence on employees’ performance in information technology sector. AIP Conference Proceedings, 2587, 020004. https://doi.org/10.1063/5.0150403

[40] Akrich, M. (1992). The de-scription of technical objects. In W. E. Bijker & J. Law (Eds.), Shaping technology/building society: Studies in sociotechnical change (pp. 205–224). MIT Press.

[41] Srnicek, N. (2017). Platform capitalism. Polity Press.

[42] Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. fairmlbook.org.

Downloads

Published

13-08-2026

How to Cite

Artificial Intelligence Agents, Capital, and Symbolic Violence: Toward a Field Theory of AI in the Social Sciences. (2026). Journal of Multidisciplinary Sciences, Arts and Humanities, 1(1), 56-63. https://ijpress.com/jmsah/article/view/4

Similar Articles

You may also start an advanced similarity search for this article.