mlsanityData to 14 Oct 2026

Artificial Intelligence and Aesthetic Judgment

arXiv 2309.12338 ↗First posted 21 Aug 2023cs.CY cs.AIcited by 3

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Abstract (arXiv)

Generative AIs produce creative outputs in the style of human expression. We argue that encounters with the outputs of modern generative AI models are mediated by the same kinds of aesthetic judgments that organize our interactions with artwork. The interpretation procedure we use on art we find in museums is not an innate human faculty, but one developed over history by disciplines such as art history and art criticism to fulfill certain social functions. This gives us pause when considering our reactions to generative AI, how we should approach this new medium, and why generative AI seems to incite so much fear about the future. We naturally inherit a conundrum of causal inference from the history of art: a work can be read as a symptom of the cultural conditions that influenced its creation while simultaneously being framed as a timeless, seemingly acausal distillation of an eternal human condition. In this essay, we focus on an unresolved tension when we bring this dilemma to bear in the context of generative AI: are we looking for proof that generated media reflects something about the conditions that created it or some eternal human essence? Are current modes of interpretation sufficient for this task? Historically, new forms of art have changed how art is interpreted, with such influence used as evidence that a work of art has touched some essential human truth. As generative AI influences contemporary aesthetic judgment we outline some of the pitfalls and traps in attempting to scrutinize what AI generated media means.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 78 references that are in the map. First posted on arXiv 21 Aug 2023.

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20 of 78 references.

References · 20

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  3. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  5. 2022Emergent Abilities of Large Language Modelscited by 2,512
  6. 2018Datasheets for Datasetscited by 1,386
  7. 2018Model Cards for Model Reportingcited by 1,080
  8. 2022In-context Learning and Induction Headscited by 964
  9. 2022Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetscited by 629
  10. 2023Erasing Concepts from Diffusion Modelscited by 539
  11. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
  12. 2019Measurement and Fairnesscited by 308
8 more
  1. 2023Glaze: Protecting Artists from Style Mimicry by Text-to-Image Modelscited by 205
  2. 2021The Values Encoded in Machine Learning Researchcited by 172
  3. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  4. 2023Art and the science of generative AI: A deeper divecited by 95
  5. 2021Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Developmentcited by 85
  6. 2022In conversation with Artificial Intelligence: aligning language models with human valuescited by 62
  7. 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Failcited by 38
  8. 2020Artificial Intelligence is stupid and causal reasoning won't fix itcited by 20

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