Artificial Intelligence in Enforcement: Epistemological Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The presented study examines the epistemological and philosophical and legal problems of the introduction of artificial intelligence systems in law enforcement. The article discusses the problematic implementation and use of artificial intelligence to automate the enforcement process, the judiciary and public administration. It is shown that the latter is considered without taking into account a key factor - the specifics of the intellectual process of bringing the general norm to a particular case. The authors show that for artificial intelligence systems, the contextuality of the principles of law is not achievable, while it is extremely necessary in law enforcement. In AI, contextual intellectual procedures cannot be programmed, since the ratio between the norm and the context of its interpretation involves a break through the hermeneutic circle in which the norm is a part and the context of the norm (industry principles) is a whole. The limited possibilities of using artificial intelligence systems in justice are also discussed, it is proved that digitalization in this area will be only instrumental in nature, and the administrative functions of robotic technologies are quite problematic and generally ineffective in the spiritual, moral and ethical dimension.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it