Unleashing the Power of Artificial Intelligence in Criminal Liability Determination in the Modern Police System with Special Reference to its Application in Combating Fishery-Related Crimes in India
Bibliographic record
Abstract
Police play a pivotal role in various ways to determine criminal liability in any given system of law.Police, policing, criminal liability, and criminal justice delivery system in general have witnessedrapid change in the 21st Century, especially due to globalization and the unprecedented growth ofscientific and technological developments which in turn needs the adoption of modern technologiesand tools to deal and regulate the same otherwise the very purpose of police and policing will bedefeated as it will become out-dated to deal with the modern crimes and criminals. The fisheriessector in India faces significant challenges due to rampant illegal practices, including illegal fishing,overfishing, and the trading of endangered species. These activities not only deplete marineresources but also have severe economic and environmental consequences. Traditional monitoringand enforcement methods have proven to be inadequate in curbing such offenses. This paper strivesto highlight two significant aspects viz., a. latest policies, initiatives, and practices adopted byvarious major governments around the world related to artificial intelligence to improve the nuancesin fixing criminal liability in the criminal justice delivery system, in general, b. the significant roleof artificial intelligence in combating fishery-related offenses and crimes in India, in particular.Along with that, this paper seeks to put forward suggestions to avoid the existing lacunas and forbest practices to be adopted by Indian law enforcement agencies in detecting, preventing, andinvestigating various crimes including fishery-related crimes. This paper also encourages furtherresearch.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".