The ethics of creating artificial superintelligence: a global risk perspective
Bibliographic record
Abstract
Abstract As artificial intelligence (AI) continues its exponential growth and nears the threshold of artificial general intelligence (AGI), it is timely and urgent to initiate reflections on artificial superintelligence (ASI), which may emerge rapidly after AGI. While ASI remains hypothetical, its potential emergence could be abrupt and profoundly transformative, necessitating proactive ethical and strategic inquiry. This paper proposes a multidimensional reflection on ASI, not only in its technical form but also in relation to humanity and the planetary context. It seeks to answer the question: “Should Homo sapiens develop an artificial superintelligence on their planet?” The paper introduces key definitions, outlines major existential risks to humanity and the biosphere, and considers whether ASI could mitigate these threats. It ultimately proposes a conceptual equation to assess the potential net impact of ASI, and introduces an original Venn diagram that classifies problem domains across AI, AGI, and ASI. Together, these tools aim to advance theoretical understanding and guide future inquiry into the core research question.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.051 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".