Ethical Governance of AI in Public Policy: Bridging Law, Philosophy, and Data Science
Bibliographic record
Abstract
As AI moves rapidly into public policy, addressing the ethical and legal questions related to AI is now a bigger challenge. This paper highlights the main requirement of ethical AI by using law, philosophy and data science. It helps public bodies make decisions that are fair, accountable, clear and human rights friendly. The analysis covers main international policy frameworks as well as European Union, UNESCO, US, Indian and Canadian national strategies. During this research, multiple case studies were carried out while exploring concepts from both deontology and utilitarianism in philosophical ethics. It is claimed in the study that algorithms, prejudiced information, inefficient communication to the public and absent regulations lead to serious governance issues. This work suggests a model where ethics in design, strong regulations and the involvement of the public guide data governance. This means that you must combine different areas, support each other and truly examine the audit trail to understand everything. According to the study, to include ethics in AI for public policy, all parties, including technology, principles and institutions, should join forces to safekeep the public.
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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 | 0.096 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".