Perceptions and Realities of Text-to-Image Generation
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
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
- 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
- 2022The Creativity of Text-to-Image Generationcited by 73
- 2022A Taxonomy of Prompt Modifiers for Text-To-Image Generationcited by 55
- 2023Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineeringcited by 43
- 2019Value-laden Disciplinary Shifts in Machine Learningcited by 25
- 2023Using Text-to-Image Generation for Architectural Design Ideationcited by 16
- 2021Ethics and Creativity in Computer Visioncited by 15
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 2.
5 papers.
- 2025POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generationcited by 6
- 2023The Cultivated Practices of Text-to-Image Generationcited by 3
- 2025Unlimited Editions: Documenting Human Style in AI Art Generationcited by 1
- 2026Toward Trustworthy Portrait Editing: Evaluation of Demographic Misrepresentation in I2I Modelscited by 1
- 2026Interrogating Design Homogenization in Web Vibe Codingcited by 1
- 2026Interrogating Design Homogenization in Web Vibe Coding13 Mar 2026
- 2026Toward Trustworthy Portrait Editing: Evaluation of Demographic Misrepresentation in I2I Models18 Feb 2026
- 2025Unlimited Editions: Documenting Human Style in AI Art Generation5 Jun 2025
- 2025POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation18 Apr 2025
- 2023The Cultivated Practices of Text-to-Image Generation20 Jun 2023