Exploring the Challenges of Artificial Intelligence in Data Integrity and its Influence on Social Dynamics
Bibliographic record
Abstract
This study examines the ethical challenges and regulatory dynamics of Artificial Intelligence (AI) in relation to data integrity and its influence on social dynamics. Employing a cross-sectional survey approach, primary data was collected from 650 AI practitioners across various sectors, encompassing developers, data scientists, ethicists, and policymakers. The study investigated the correlations between regulatory compliance, ethical awareness, professional training, and experience in AI practice with the effectiveness of AI implementation and data integrity. The findings revealed a strong positive correlation between higher levels of regulatory compliance and perceived effectiveness in AI implementation, as well as between AI ethics awareness and data integrity assurance. Moreover, a significant relationship was observed between professional training in AI and its positive impact on social dynamics. However, experience in the AI field, while positively correlated, showed a weaker link to data integrity, indicating that experience alone is insufficient for ensuring effective AI practices. The study highlights the importance of ethical considerations, regulatory frameworks, and professional training in shaping AI development and its societal implications. The need for dynamic, adaptable, and inclusive regulatory frameworks that can align AI practices with societal values and ethical norms is emphasized. Future research directions include exploring AI ethics and regulation in diverse cultural contexts and the impact of emerging technologies like quantum computing on AI ethics.
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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 | 0.032 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".