Perceptions of AI Ethics Policies Among Scientists and Engineers in Policy-Related Roles: An Exploratory Investigation
Bibliographic record
Abstract
Abstract The explosive growth of artificial intelligence (AI) over the past few years has focused attention on how diverse stakeholders regulate these technologies to ensure their safe and ethical use. Increasingly, governmental bodies, corporations, and nonprofit organizations are developing strategies and policies for AI governance. While existing literature on ethical AI has focused on the various principles and guidelines that have emerged as a result of these efforts, just how these principles are operationalized and translated to broader policy is still the subject of current research. Specifically, there is a gap in our understanding of how policy practitioners actively engage with, contextualize, or reflect on existing AI ethics policies in their daily professional activities. The perspectives of these policy experts towards AI regulation generally are not fully understood. To this end, this paper explores the perceptions of scientists and engineers in policy-related roles in the US public and nonprofit sectors towards AI ethics policy, both in the US and abroad. We interviewed 15 policy experts and found that although these experts were generally familiar with AI governance efforts within their domains, overall knowledge of guiding frameworks and critical regulatory policies was still limited. There was also a general perception among the experts we interviewed that the US lagged behind other comparable countries in regulating AI, a finding that supports the conclusion of existing literature. Lastly, we conducted a preliminary comparison between the AI ethics policies identified by the policy experts in our study and those emphasized in existing literature, identifying both commonalities and areas of divergence.
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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 | 0.036 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".