Moral circle expansion: A promising strategy to impact the far future
Bibliographic record
Abstract
Many sentient beings suffer serious harms due to a lack of moral consideration. Importantly, such harms could also occur to a potentially astronomical number of morally considerable future beings. This paper argues that, to prevent such existential risks, we should prioritise the strategy of expanding humanity’s moral circle to include, ideally, all sentient beings. We present empirical evidence that, at micro- and macro-levels of society, increased concern for members of some outlying groups facilitates concern for others. We argue that the perspective of moral circle expansion can reveal and clarify important issues in futures studies, particularly regarding animal ethics and artificial intelligence. While the case for moral circle expansion does not hinge on specific moral criteria, we focus on sentience as the most recommendable policy when deciding, as we do, under moral uncertainty. We also address various nuances of adjusting the moral circle, such as the risk of over-expansion.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".