Respecting cultural diversity in ethics applied to AI : a new approach for a multicultural governance
Bibliographic record
Abstract
Artificial intelligence seems to be part of our everyday lives. For some it represents the promise of a better world and many improvements that would be beneficial for humanity. For others, AI is seen as threat, if not an existential threat that needs to be controlled strictly. Whatever the stance, the need to regulate AI is now widely recognized. Short of legal instruments offering a specific framework for the development and use of AI, ethics has been summoned to set standards and establish guardrails. Yet, the number of documents pertaining to ethical standards for AI has increased exponentially to reach a point where it is difficult to know how to use them efficiently. These documents have mostly been issued to promote vested interests, and the setting of a universal code of AI ethics has been seen as a solution for AI global governance. If a global governance system is required to avoid negative outcomes of AI, it appears that the idea of a universal code of ethics denies the diversity of ethical standpoints based on the diversity of philosophical cultures the world is made of. Instead of offering a legitimate and efficient tool, such a solution could lead to cultural tensions between leading actors in the field of AI as it is the case between China and the United States. To avoid conflicting situations stemming from the denial of cultural diversity, it is more than ever necessary to put aside the idea of a universal code of AI
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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 | 0.049 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.090 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".