Les enjeux identitaires de l'humain dans le débat philosophique sur la robotique humanoïde et l'amélioration humaine
Bibliographic record
Abstract
Do human identity representations (human identity, natural/artificial distinction) still make sense in the context of the development of humanoid robotics (humanizing the robot) and human enhancement (automation of the human)? The problem is that critical philosophers, like Lin and Allhoff who founded the journal NanoEthics, challenge these representations of human identity, as if the discussion of the ethical evaluation of these representations was exhausted with regards to the two issues that they raise in 2009 in Ethics of Human Enhancement: 25 Questions and Answers, i.e.: “Does the notion of human dignity suffer with human enhancements?” and “Is the natural-artificial distinction morally significant in this debate?” The purpose of this article is to show – in light of various texts that constitute our framework for analyzing moral arguments – the limits of the scope and insufficiency of the critical arguments that Lin and Allhoff use to answer these two questions. But in applying our framework to these authors, we will also show how the question of human identity or the natural/artificial distinction still makes sense in the ethical evaluation.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".