The Crisis of Social Categories in the Age of AI


  • Jean-Marie John-Mathews Sciences Po; Université Paris-Saclay
  • Dominique Cardon Sciences Po; Université Paris Est



AI, Machine learning, Social categories


This article explores the change in calculation methods induced by deep learning techniques. While more traditional statical methods are based on well instituted categories to measure the social world, these categories are today denounced as a set of hardened and abstract conventions that are incapable of conveying the complexification of social life and the singularities of individuals. Today AI models try to overcome some criticism raised by rigid social categories by combining a "spatial and temporal expansion" of the data space, producing a global transformation of the calculation methods.


Abbott, A. (1988). Transcending General Linear Reality. Sociological Theory, 6(2), 169–186.

Angeletti, T. (2011). Faire la réalité ou s’y faire. La modélisation et les déplacements de la politique économique au tournant des années 70. Politix, 3(95), 47–72. 10.3917/pox.095.0047

Boltanski, L. (2014). Quelle statistique pour quelle critique?. In I. Bruno, E. Didier & J. Prévieux (Eds.), Statactivisme. Comment lutter avec des nombres?. Paris: Zones.

Brayne, S., & Christin, A. (2020). Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts. Social Problems, 68(3), 608–624. 10.1093/socpro/spaa004

Breiman, L. (2001). Statistical Modeling: The Two Cultures. Statistical Science, 16(3), 199–215.

Bruno, I., Didier, E., & Prévieux, J. (Eds.). (2015). Statactivisme: Comment lutter avec des nombres. Paris: Zones.

Cardon, D., Cointet, J.-P., & Mazières, A. (2018). Neurons Spike Back. The Invention of Inductives Machines and the Artificial Intelligence Controversy. Réseaux, 5(211), 173–220.

Cevolini, A., & Esposito, E. (2020). From Pool to Profile: Social Consequences of Algorithmic Prediction in Insurance. Big Data & Society, 7(2). 2053951720939228

Cheney-Lippold, J. (2017). We are Data. Algorithms and the Making of our Digital Selves. New York, NY: New York University Press.

Cornuéjols, A., Miclet, L. & Barra, V. (2018). Apprentissage Artificial: Deep Learning, Concepts et Algorithmes, 3 édition. Paris: Eyrolles.

Crawford, K., & Calo, R. (2016). There Is a Blind Spot in AI Research. Nature, 538, 311–313.

Deleuze, G. (1990). Post-scriptum sur les sociétés de contrôle. L’autre journal, 1.

Desrosières, A. (2014). Prouver et gouverner. Une analyse politique des statistiques publiques.

Paris: La découverte.

Desrosières, A., & Thévenot, L. (1988). Les catégories socioprofessionnelles. Paris: La découverte.

Eubanks, V. (2017). Automating Inequality, How High-tech Tools Profile, Police, and Punish the Poor. New York, NY: St. Martin’s Press.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press, Cambridge, MA.

Haggerty, K.D, & Ericson, R.V. (2000). The Surveillant Assemblage. British Journal of Sociology, 51(4), 605–622.

Hahn, R.W., & Tetlock, P.C. (2005). Using Information Markets to Improve Public Decision Making. Harvard Journal of Law and Public Policy, 29(1), 213–289.

Harcourt, B. (2015). Exposed. Desire and Disobedience in the Digital Age. Cambridge, MA: Harvard University Press.

Harcourt, B. (2006). Against Prediction: Profiling, Policing and Punishing in an Actuarial Age. Chicago, IL: University of Chicago Press.

Hénin, C. (2021). Confier une décision vitale à une machine. Réseaux, 225(1), 187–213. https: //

Hofmann, H.J. (1990). Die Anwendung des CART-Verfahrens zur statistischen Bonitätsanalyse von Konsumentenkrediten. Zeitschrift fur Betriebswirtschaft, 60, 941–962.

Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389–399.

John-Mathews, J.-M. (2021). Critical Empirical Study on Black-box Explanations in AI. arXiv 2109.15067.

John-Mathews, J.-M. (2022). Some Critical and Ethical Perspectives on the Empirical Turn of AI Interpretability. Technological Forecasting and Social Change,< em>174, 121209. https: //

John-Mathews, J.-M., Cardon, D., & Balagué, C. (2022). From Reality to World. A Critical Perspective on AI Fairness. Journal of Business Ethics, 178, 945–959. 1007/s10551-022-05055-8

John-Mathews, J.-M., De Mourat, R., De Ricci, M., & Crépel, M. (2023). Re-enacting Machine Learning Practices to Inquire into the Moral Issues They Pose. Convergence, forthcoming.

Koopman, C. (2019). How We Became Our Data. A Genealogy of the Informational Person. Chicago: The University of Chicago Press.

Krasmann, S. (2020). The Logic of the Surface: On the Epistemology of Algorithms in Times of Big Data. Information, Communication & Society, 23(14), 2096–2109. 10.1080/1369118X.2020.1726986

Le Bras, H. (2000). Naissance de la mortalité. L’origine politique de la statistique et de la démographie. Paris: Seuil/Gallimard.

Lu, T., Zhang, Y., & Li, B. (2019). The Value of Alternative Data in Credit Risk Prediction: Evidence from a Large Field Experiment, ICIS 2019 Conference, Munich, December.

Lyon, D. (2001). Surveillance Society: Monitoring Everyday Life. Buckingham: Open University Press.

Lury, C., & Day, S. (2019). Algorithmic Personalization as a Mode of Individuation. Theory, Culture & Society,36(2), 17–37.

Martuccelli D. (2010). La société singulariste. Paris: éd. Armand Colin.

Mau, S. (2019). The Metric Society. On the Quantification of the Social. Cambridge, MA: Polity Press.

Mitchell, T. (1997). Machine Learning. Boston, MA: McGraw-Hill.

Murphy, M. (2017). The Economization of Life. Durham, NC: Duke University Press.

Olah, C., Mordvintsev, A., & Schubert, L. (2017). Feature Visualization. Distill, 2(11).

Rogers, R. (2009). Post-Demographic Machines. In A. Dekker & A. Wolfsberger (Eds.), Walled Garden (pp. 344–355). Amsterdam: Virtual Platform.

Rouvroy, A., & Berns, T. (2013). Gouvernementalité algorithmique et perspectives d’émancipation. Réseaux, 1, 163–196.

Rouvroy, A., & Berns, T. (2013). Algorithmic governmentality and prospects of emancipation. Disparateness as a precondition for individuation through relationships?. Réseaux, 177(1), 163–196."

Salganik, M.J., Lundberg, I., Kindel, A.T., Ahearn, C.E., Al-Ghoneim, K., Almaatouq, A., Altschul, D.M., Brand, J.E., Carnegie, N.B., Compton, R.J., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B.J., Jahani, E., Kashyap, R., Kirchner, A., McKay, S., ... McLanahan, S. (2020). Measuring the Predictability of Life Outcomes with a Scientific Mass Collaboration. Proceedings of the National Academy of Sciences of the United States of America, 117(15), 8398–8403.

Solove, D. (2004). The Digital Person: Technology and Privacy in the Information Age. New York, NY: New York University Press.

Terranova, T. (2004). Network Culture: Politics for the Information Age. London: Pluto.




How to Cite

John-Mathews, J.-M., & Cardon, D. (2022). The Crisis of Social Categories in the Age of AI. Sociologica, 16(3), 5–16.