The Aesthetic Ethics of Midjourney under the Development of Artificial Intelligence
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
With the continuous development of artificial intelligence technology, artificial intelligence mapping applications such as Midjourney are also playing an increasingly important role in our daily life and work. However, with the expansion of its application, it also faces a series of ethical problems. From the perspective of aesthetic ethics, this paper discusses the aesthetic ethics of Midjourney, an artificial intelligence form, in artistic creation. Through the discussion of the aesthetic ethics of Midjourney, the author puts forward the creation criteria to be followed when interacting with Midjourney, and pays attention to the importance of Midjourney database specification. In modern society, people have paid more and more attention to the appropriate field of application of artificial intelligence, requiring Midjourney to follow the originality of artistic creation as much as possible, respect for human creation laws and other ethics. Therefore, it has become an important research direction to provide suggestions on aesthetic ethics for Midjourney, so as to help Midjourney achieve art generation conforming to ethical norms. Through the way of thinking of artificial intelligence aesthetic ethics, we can provide new ideas and methods for the development of Midjourney, so that it can better serve human society.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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