IDEAS home Printed from https://ideas.repec.org/p/osf/mediar/t7kvz_v1.html

Forcing generalization: technical art as (synthetic) data work

Author

Listed:
  • Steinhoff, James

    (University College Dublin)

Abstract

Synthetic data has recently been proposed as an alternate means of procuring data for training AI which dispenses with data work. However the labour required to produce it has not been studied. This paper does so by looking at the technical artist: a hybrid programmer and 3D artist recently brought into the AI industry from the games and film industry. I argue that technical art, in the synthetic data context, is data work but of an unfamiliar kind. I demonstrate this through a labour process analysis of procedural asset creation. I show that in the synthetic data context, technical art is governed by the goal of forcing generalization. I suggest that the concept of data work should not ossify to capture only its present state of collection and cleaning, but that a more mutable concept is necessary to track changes in the AI industry. While claims of data work’s coming disappearance are implausible, it seems unwise to overstate its permanency in its present state.

Suggested Citation

  • Steinhoff, James, 2026. "Forcing generalization: technical art as (synthetic) data work," MediArXiv t7kvz_v1, Center for Open Science.
    • Handle: RePEc:osf:mediar:t7kvz_v1
    DOI: 10.31219/osf.io/t7kvz_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/6a0d8490acbef6a10d8ecaac/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/t7kvz_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Paola Tubaro & Antonio A. Casilli & Marion Coville, 2020. "The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence," Post-Print hal-02554196, HAL.
    2. Jill Walker Rettberg, 2020. "Situated data analysis: a new method for analysing encoded power relationships in social media platforms and apps," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    3. Sergey I. Nikolenko, 2021. "Synthetic Data for Deep Learning," Springer Optimization and Its Applications, Springer, number 978-3-030-75178-4, January.
    4. Ilia Shumailov & Zakhar Shumaylov & Yiren Zhao & Nicolas Papernot & Ross Anderson & Yarin Gal, 2024. "AI models collapse when trained on recursively generated data," Nature, Nature, vol. 631(8022), pages 755-759, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Robin Dirk & Jonas L Fischer & Simon Schardt & Markus J Ankenbrand & Sabine C Fischer, 2023. "Recognition and reconstruction of cell differentiation patterns with deep learning," PLOS Computational Biology, Public Library of Science, vol. 19(10), pages 1-29, October.
    2. Yang Shen, 2024. "Future jobs: analyzing the impact of artificial intelligence on employment and its mechanisms," Economic Change and Restructuring, Springer, vol. 57(2), pages 1-33, April.
    3. Andrew Delios & Rosalie L. Tung & Arjen Witteloostuijn, 2025. "How to intelligently embrace generative AI: the first guardrails for the use of GenAI in IB research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 56(4), pages 451-460, June.
    4. Najmeh Samadiani & Amanda S. Barnard & Dayalan Gunasegaram & Najmeh Fayyazifar, 2025. "Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4477-4517, October.
    5. Nguyen, Manh-Hung, 2026. "Epistemic Capital and Two-Trap Growth in the AI Era," TSE Working Papers 26-1722, Toulouse School of Economics (TSE).
    6. Grohmann, Rafael & Pereira, Gabriel & Guerra, Abel & Abilio, Ludmila Costhek & Moreschi, Bruno & Jurno, Amanda, 2022. "Platform scams: Brazilian workers’ experiences of dishonest and uncertain algorithmic management," LSE Research Online Documents on Economics 115622, London School of Economics and Political Science, LSE Library.
    7. Hongshen Sun & Juanjuan Zhang, 2025. "From Model Choice to Model Belief: Establishing a New Measure for LLM-Based Research," Papers 2512.23184, arXiv.org.
    8. Paola Tubaro & Antonio A Casilli, 2024. "Who bears the burden of a pandemic? COVID-19 and the transfer of risk to digital platform workers," Post-Print hal-03369291, HAL.
    9. Shalpegin, Timofey & Browning, Tyson R. & Kumar, Ajay & Shang, Guangzhi & Thatcher, Jason & Fransoo, Jan C. & Holweg, Matthias & Lawson, Benn, 2025. "Generative AI and empirical research methods in operations management," Other publications TiSEM 0eb52ee8-35d0-4c97-8732-8, Tilburg University, School of Economics and Management.
    10. Konstantinos Pouliakas & Giulia Santangelo & Paul Dupire, 2025. "Are artificial intelligence skills a reward or a gamble? Deconstructing the AI wage premium in Europe," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 15(4), pages 1091-1128, December.
    11. Heikkilä, Jussi T. S., 2024. "Human intelligence versus artificial intelligence in classifying economics research articles: exploratory evidence," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 81(7), pages 18-30.
    12. Newlands, Gemma & Lutz, Christoph, 2024. "Mapping the prestige and social value of occupations in the digital economy," Journal of Business Research, Elsevier, vol. 180(C).
    13. Messner, Wolfgang, 2025. "Quantification of cultural practices and diversity: An empirical experiment with generative artificial intelligence," Journal of World Business, Elsevier, vol. 60(3).
    14. Xiaomeng Zhu & Pär Mårtensson & Lars Hanson & Mårten Björkman & Atsuto Maki, 2025. "Automated assembly quality inspection by deep learning with 2D and 3D synthetic CAD data," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2567-2582, April.
    15. José A. Torres-León & Marco A. Moreno-Armendáriz & Hiram Calvo, 2024. "Representing the Information of Multiplayer Online Battle Arena (MOBA) Video Games Using Convolutional Accordion Auto-Encoder (A 2 E) Enhanced by Attention Mechanisms," Mathematics, MDPI, vol. 12(17), pages 1-19, September.
    16. Teppo Felin & Matthias Holweg, 2024. "Theory Is All You Need: AI, Human Cognition, and Causal Reasoning," Strategy Science, INFORMS, vol. 9(4), pages 346-371, December.
    17. Julian Lehmann & Hans Berends & Nicholas Berente & Sanja Tumbas, 2026. "Architectural Learning in the Professional 3D Printing Ecosystem: Proxies and the Complementarity Space," Organization Science, INFORMS, vol. 37(1), pages 157-185, January.
    18. Plantin, Jean-Christophe, 2021. "The data archive as factory: alienation and resistance of data processors," LSE Research Online Documents on Economics 109692, London School of Economics and Political Science, LSE Library.
    19. Alvaro Figueira & Bruno Vaz, 2022. "Survey on Synthetic Data Generation, Evaluation Methods and GANs," Mathematics, MDPI, vol. 10(15), pages 1-41, August.
    20. Takuma Tanaka, 2025. "Mean-reverting self-excitation drives evolution: phylogenetic analysis of a literary genre, waka, with a neural language model," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-10, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:mediar:t7kvz_v1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://mediarxiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.