Reconcilable Differences: Comparative Analysis of <scp>EU</scp> and <scp>US</scp> Ethical <scp>AI</scp> Frameworks with Focus on Divergent Ethical Aspects
Bibliographic record
Abstract
ABSTRACT The impact of AI on information environments has prompted questions around its ethical regulation, and alignment with the EU AI Act is increasingly necessary. As the first AI regulation in the world, combined with the Brussels effect, the EU is a global AI regulatory leader. This context is compounded by the volatility of other global powers. Information sciences can make unique contributions to policy development with its focus at the intersection of information, technology, and people. This paper reports on the second phase of a project, initiated in 2023, analyzing ethical similarities and differences between the EU's Ethics Guidelines for Trustworthy AI and the US' AI Bill of Rights, using qualitative content analysis. Findings demonstrate that ethical differences can be resolved while accounting for similarities. Implications suggest collective need for international cooperation and compliance. This paper provides a case study for detailed info‐ethical analysis for regulatory alignment.
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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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".