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Forcing generalization: technical art as (synthetic) data work

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  • 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
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    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, April.
    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.
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