The Impact of Awareness of New Artificial Intelligence Technologies on Policy Governance on Risk
Bibliographic record
Abstract
Background/Objectives: This study examined the risks of new AI technologies and their impact on policy governance. Artificial intelligence is bringing about changes in various fields such as politics, economy and culture through information society and technology. In particular, it has a positive effect on solving various problems of existing society and overcoming limitations. But this advancement in artificial intelligence can create the opposite problem as expected. This appears to be a risk. We identify the factors that recognize this risk and investigate the possible impact on government governance.Methods/Statistical analysis: The questionnaire and data of this journal were analyzed by Korean public portal data, and the analysis data were designated by the Korea Information Technology Agency, AI related company, AI association, Ministry of Science, ICT and Future Planning, IT society, government research institute, Korea Communications Commission, and National Security Agency The questionnaire survey was based on AI experts working in the field.The analysis program uses IBM SPSS Statistics 22. The analysis methods are descriptive statistical analysis, reliability analysis and exploratory factor analysis.Findings: This study examined the risks of new AI technologies and their impact on policy governance. The survey was conducted to clarify comments on awareness of new AI-related technologies, awareness of AI risks, and improvements to AI-related policies.AI risk has become an integral part of regulation and the government's role as a risk manager is important.Improvements/Applications: Further discussion is needed regarding the commercialization effects of AI technology awareness, benefit items and timing items on policy governance through risk awareness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".