A Study on Ethical Awareness Changes and Education in Artificial Intelligence Society
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
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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it