Embedding Ethics Into Artificial Intelligence: Understanding What Can Be Done, What Can't, and What Is Done
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
Embedding ethical considerations within the development of AI driven technologies becomes more and more pressing as new technologies are developed. Given the impact of autonomous technologies on individuals and society, it is worth taking the time to assess and manage the ethical aspects and possible consequences of our technological endeavors. While the growing rapidity of autonomous decision processes makes it hard to keep individuals in the decision loops, people are turning their attention to the ways in which ethics could be integrated to machines and algorithms, as well as to the possibility of defining autonomous ethical machines that would be able to solve ethical dilemmas and act ethically (e.g. autonomous vehicles). Notwithstanding theoretical and practical difficulties surrounding the possibility of defining such ethical machines, important elements should be considered when reflecting on the embedding of ethics into AI technologies. The present paper aims to critically analyze the limitations of such endeavors by exposing common misconceptions relating to AI ethics.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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