mlsanityData to 14 Oct 2026

Perceptions and Realities of Text-to-Image Generation

arXiv 2306.08363 ↗First posted 14 Jun 2023cs.HC cs.AI cs.CYcited by 5

Not on the map. Why?

Abstract (arXiv)

Generative artificial intelligence (AI) is a widely popular technology that will have a profound impact on society and individuals. Less than a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on many creative domains. In this paper, we present the findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their fears and concerns, and thoughts about risks and dangers of text-to-image generation to the individual and society. We find that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk. The risks for others were more easy to recognize for participants. Artists were particularly seen at risk. Interestingly, participants who had tried the technology rated its future importance lower than those who had not tried it. This result shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 10 of its 56 references that are in the map. First posted on arXiv 14 Jun 2023.

Builds on

10 of 56 references.

References · 10

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  3. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  4. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  5. 2022The Creativity of Text-to-Image Generationcited by 73
  6. 2022A Taxonomy of Prompt Modifiers for Text-To-Image Generationcited by 55
  7. 2023Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineeringcited by 43
  8. 2019Value-laden Disciplinary Shifts in Machine Learningcited by 25
  9. 2023Using Text-to-Image Generation for Architectural Design Ideationcited by 16
  10. 2021Ethics and Creativity in Computer Visioncited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 2.

5 papers.