A Study on Ethical Awareness Changes and Education in Artificial Intelligence Society
Bibliographic record
Abstract
In order to change our moral practice and contemplative consciousness during the change to the Artificial Intelligence society, Artificial Intelligence ethics education is necessary. Artificial Intelligence ethics education should aim to form moral human beings so that members of the Artificial Intelligence society can grow into moral subjects. Key elements of responsibility and safety, employment and discrimination, and tolerance and limitations were derived as core elements of Artificial Intelligence ethics education. Based on the derived core elements, the Artificial Intelligence ethics training course was constructed, and after the 14th week of learning, the change in learners' Artificial Intelligence ethics awareness was measured. As a result of the measurement, the improvement effect through Artificial Intelligence education was evident in responsibility and safety, tolerance and limit, but not in employment and differentiation. The purpose of this study is to present a direction for Artificial Intelligence ethics education by examining the educational values and limitations of Artificial Intelligence ethics education, and that Artificial Intelligence ethics education is necessary for members of the Artificial Intelligence society to grow into moral subjects.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".