The Impact of Applying Artificial Intelligence on the Quality of Decision-Making of Abu Dhabi Police General Headquarters
Bibliographic record
Abstract
The study aims to examine the impact of applying artificial intelligence on the quality of making decisive security decisions in Abu Dhabi Police General Headquarters. The study uses hypothetical deductive approach to measure impact of applying artificial intelligence on the quality of decision-making. The study uses purposive sample of 100 respondents on staff of Abu Dhabi Police General Headquarters. The results showed that the importance of artificial intelligence was high. This indicates that managerial decision-making is directly or indirectly affected by artificial intelligence within the study sample. This makes the security sectors interested in the developments of artificial intelligence and its outputs and exploits them to save time in making decisions, and achieving quality and acceptance. The results further showed that the level of agreement on administrative decision-making has positive and significant relationship on System Sustainability and Development; Effectiveness of the program used as well as security system of Abu Dhabi Police General Headquarters. Artificial intelligence has become one of the main mechanisms that security sectors rely on in decision-making and represent the most important pillars of development that is indispensable under the changing strong competitions in the world of management, as the information and digital revolution cannot be overlooked and difficult to keep up with at the present time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".