Conative Dimensions of Machine Ethics: A Defense of Duty
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
Immanuel Kant is one of the giants of moral theorizing in the western philosophical tradition. He developed a view of moral imperatives and duty that continues to inspire thought up to the present. In a thought-provoking series of papers, Anthony Beavers argues that Kant's conception of morality will not be applicable to machines. In other words, it will turn out that when we design machines at a level of sophistication such that ethical constraints must be built into their behavior, Kant's understanding of morality will not be helpful. Specifically, the notion of duty as involving some sort of internal conflict can be jettisoned. The argument in this paper is that there are aspects of duty that can be preserved for machine ethics. The goal will not be to defend any of the details of Kant's position. Rather, it is to motivate some ways of thinking about duty that may be useful for machine 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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