Bibliographic record
Abstract
The ethics of artificial intelligence is the response to a new dilemma that demands international society to provide a legal response to the many ethical challenges artificial intelligence creates. COVID-19 accelerates the use of AI in all countries and all fields. The pandemic is accelerating the transition to a society that is increasingly based on the use of, and reliance on, AI, and this also enhances the threats and creates new risks related to human rights. Artificial Intelligence (AI) influences human rights and international humanitarian law. This paper addresses international mechanisms and ethics as new rules which can ensure the protection of human rights in the age of AI. Two arguments are discussed in this study. Considering the ubiquitous and global reach of AI, the challenges it imposes requires an international legal oversight, a requirement that highlights the importance of ethical frameworks. In conclusion, the paper emphasizes how optimal action is needed to protect human rights in the age of AI. Rethinking international law and human rights and enhancing the ethical frameworks have thus become obligatory rather than a choice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".