Comparative Designs Reveal Preferences for Human-Generated Rather Than AI-Generated art
Bibliographic record
Abstract
The evaluation of AI-generated art has seen increased interest after widespread access to AI-generated art (e.g., DALL-E or Stable Diffusion). While previous studies have suggested that there are preferences for human-generated art, the research remains far from robust with numerous contradictory findings. One potential reason for this discrepancy is differing experimental designs employing comparative or non-comparative methods. To shed light on this problem, two experiments were conducted: one using a Likert scale (N = 250) and another using a 2-alternative forced choice design (N = 102). Our conflicting results between the two designs suggest that traditional Likert-based art appraisals in non-comparative formats may not be sensitive enough to reliably detect preferences that a forced-choice task can reveal. While AI-generated art continues to become more mainstream, people tend to prefer human art in terms of their liking and valuation appraisals when measured in comparative designs that better approximate real-world interaction with art.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".