mlsanityData to 7 Oct 2026

AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks

arXiv 2401.06178 ↗First posted 10 Jan 2024cs.CY cs.AIcited by 12

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Abstract (arXiv)

Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 9 of its 119 references that are in the map. First posted on arXiv 10 Jan 2024.

Builds on

9 of 119 references.

References · 9

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  5. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  6. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  7. 2023Extracting Training Data from Diffusion Modelscited by 723
  8. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 264
  9. 2023Into the LAIONs Den: Investigating Hate in Multimodal Datasetscited by 54

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 4.

12 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2023AI Imagery and the Overton Window
  2. 2025The Impact of Artificial Intelligence on Traditional Art Forms: A Disruption or Enhancement
  3. 2024Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
  4. 2026The Aura in the Machine: Genealogy and the Status of the Work of Art in the Generative Era
  5. 2024At the edge of a generative cultural precipice
  6. 2023Measuring the Success of Diffusion Models at Imitating Human Artists
  7. 2026Who Chooses the Artwork? Curatorial Agency and Distributed Intent in Botto ↗
  8. 2024No Longer Trending on Artstation: Prompt Analysis of Generative AI Art