Understanding Visual Artists’ Values and Attitudes towards Collaboration, Technology, and AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial Intelligence (AI) tools have recently gained widespread interest for image creation, but tool developers have largely focused on technical capabilities or specialized domain uses, rather than visual artists as users. We collected survey data from 89 practising visual artists and conducted follow-up interviews with 30 of them, to better understand their diverse needs and values. Through reflexive thematic analysis, we explored visual artists’ attitudes towards collaboration in art creation both with human artists and with AI- and other technology-based support systems. Our results suggest that the focus of popular AI tools on high-quality, finished images does not meet the needs of visual artists. Instead, they wanted reference images, ideation support, and variant exploration. We identified similarities and differences between how visual artists view collaboration with other artists or with machine support, enabling designers of new tools to adopt a more user-centered approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it