From Development to Dissemination: Social and Ethical Issues with Text-to-Image AI-Generated Art
Bibliographic record
Abstract
Text-to-image generative artificial intelligence (AI) have made global news headlines for not only having the ability to generate high-fidelity artworks, but also for causing increased discussion on the ethicality of its impact on living artists, the automation and commodification of art production, the frequent non-consensual collection and usage of sensitive and copyrighted images as training data, and the routinely exhibited cultural and social biases in their generated outputs.In addition, there are concerns that open-sourced text-to-image generative AI models, such as Stable Diffusion, and techniques like Textual Inversion, allow for technical restrictions on the content subject matter to be removed and for generated images to be subject specific, which could be utilized as a new medium for disinformation and sexual or targeted abuse.Because ethical discussions on AI-generated art using text-to-image generative AI models have only come to light in the last quarter of 2022, academic research on the social and ethical implications of this technology have yet to be thoroughly explored.Therefore, it is imperative for research to be done on these implications with regards to the technological development, evaluation, perception, creation, and moderation of AI-generated artworks while text-to-image generative AI systems are still in the preliminary stages of public dissemination and adoption.
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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 | 0.251 | 0.522 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".