Ethical Principles for the Development of Artificial Intelligence and its Application in Health Care Systems
Bibliographic record
Abstract
Four fundamental principles and ten ethical principles are proposed for artificial intelligence systems (AIS) in general and their application in public health. The Montreal Declaration for the Responsible Development of Artificial Intelligence (2018) on which this proposal is based is presented and commented on, as well as the UNESCO Recommendation on the Ethics of Artificial Intelligence (2022). The COVID-19 pandemic has shown the need to build a global health care system, as well as a coordinated response to the coming pandemics. The ethical principles applied to AIS can serve to reduce disparity and failures of health systems. The integration of AIS in health in different regions of the world would enable a more efficient global action, but if it is carried out from the framework of the (bio)ethical principles that are raised here: responsibility, precaution, autonomy and justice, as well as the principle of preservation of human decisions. AI can help progressively to implement a global health care system with universal and remote coverage that responds to one of the most important demands for global justice: the human right to health care.
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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 | 0.105 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.070 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".