Can There be Art Without an Artist?
Not on the map. Why?
Abstract (arXiv)
Generative AI based art has proliferated in the past year, with increasingly impressive use cases from generating fake human faces to the creation of systems that can generate thousands of artistic images from text prompts - some of these images have even been "good" enough to win accolades from qualified judges. In this paper, we explore how Generative Models have impacted artistry, not only from a qualitative point of view, but also from an angle of exploitation of artists -- both via plagiarism, where models are trained on their artwork without permission, and via profit shifting, where profits in the art market have shifted from art creators to model owners. However, we posit that if deployed responsibly, AI generative models have the possibility of being a positive, new modality in art that does not displace or harm existing artists.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 16 Sep 2022.
Builds on
13 of 26 references.
References · 13
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2014Going Deeper with Convolutionscited by 4,231
- 2014Deep Learning Face Attributes in the Wildcited by 3,679
- 2020Extracting Training Data from Large Language Modelscited by 2,144
- 2019Machine Unlearningcited by 1,020
- 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 326
1 more
- 2020Behavioral Use Licensing for Responsible AIcited by 46
Built on by
Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 1.
10 papers.
- 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 264
- 2022Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesiscited by 59
- 2024T2IShield: Defending Against Backdoors on Text-to-Image Diffusion Modelscited by 29
- 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
- 2023Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Modelscited by 7
- 2024Continuous Concepts Removal in Text-to-image Diffusion Modelscited by 6
- 2023AI Imagery and the Overton Windowcited by 3
- 2024From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spacescited by 3
- 2025Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Modelscited by 3
- 2023Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AIcited by 2
- 2025Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models29 Apr 2025
- 2024Continuous Concepts Removal in Text-to-image Diffusion Models30 Nov 2024
- 2024From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spaces1 Nov 2024
- 2024Replication in Visual Diffusion Models: A Survey and Outlook7 Jul 2024
- 2024T2IShield: Defending Against Backdoors on Text-to-Image Diffusion Models5 Jul 2024
- 2023Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI2 Aug 2023
- 2023AI Imagery and the Overton Window31 May 2023
- 2023Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models21 May 2023
- 2023Text-to-image Diffusion Models in Generative AI: A Survey14 Mar 2023
- 2022Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis4 Nov 2022