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Record W2107671794 · doi:10.1109/t-affc.2012.27

Conative Dimensions of Machine Ethics: A Defense of Duty

2012· article· en· W2107671794 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Affective Computing · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEthics and Social Impacts of AI
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDutyMoralitySophisticationArgument (complex analysis)EpistemologyPhilosophySociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.386
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it