Adventures in space racism: going beyond the Turing Test to determine AI moral standing
Bibliographic record
Abstract
In pop culture, artificial intelligences (AI) are frequently portrayed as worthy of moral personhood, and failing to treat these entities as such is often treated as analogous to racism. The implicit condition for attributing moral personhood to an AI is usually passing some form of the "Turing Test", wherein an entity passes if it could be mistaken for a human. I argue that this is unfounded under any moral theory that uses the capacity for desire as the criteria for moral standing. Though the action-based theory of desire ensures that passing a rigourous enough version of the Turing Test would be sufficient for moral personhood, that theory has unacceptable results when used in moral theory. If a desire-based moral theory is to be made defensible, it must use a phenomenological account of desire, which would make the Turing Test fail to track the relevant property.
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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 | 0.011 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".