Artificial Intelligence in Enforcement: Epistemological Analysis
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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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".