From Development to Dissemination: Social and Ethical Issues with Text-to-Image AI-Generated Art
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
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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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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