Bibliographic record
Abstract
Machines performing as artificial agents, such as autonomous vehicles, are becoming more frequent in society and are interacting directly with human beings.Because social interaction is grounded in ethical norms, artificial agents also need to behave in accordance with the ethical norms of the society in which they operate.To be accepted by society, an artificial agent must incorporate moral judgment in the performance of its tasks.Artificial moral agents would, therefore, be seen as safe and courteous in their daily intercourse with people.To accomplish this, requires a methodology that can communicate ethical values from an ethical domain of discourse to the scientific domain of engineering design and development.I present an innovative approach, which incorporates the concept of an oracle used to interface an ethical evaluation process to a Test-Driven Development methodology employed in the design and develop an ethical machine.The oracle is the repository of moral values received from the ethical evaluation process that, in turn, become specifications for developing a machine's moral capacity to assess its actions.By morally ameliorating an artificial agent's tasking, the machines actions assume an ethical quality.The result is that an artificial agent's actions are deemed to be morally acceptable, and therefore, it becomes an ethical machine.
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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 | 0.026 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".