Agentic AI and Police Crime Prediction Using Deep Learning
Bibliographic record
Abstract
Urban policing faces growing challenges in anticipating and mitigating crime amidst dynamic, resource-constrained environments. This paper introduces an agentic AI framework that integrates deep spatio-temporal forecasting with adaptive patrol planning to support proactive public safety strategies. Leveraging over 15 years of crime data from the Seattle Police Department, we train and compare multiple deep learning architectures—including LSTM, GRU, and 1D CNN models—to predict short-term spatial distributions of crime risk. The agentic layer transforms these predictions into autonomous decisions using reinforcement-inspired routing algorithms and a feedback module that refines the predictive model based on observed outcomes. Experimental results demonstrate that GRU-based predictors strike the best balance between accuracy and computational efficiency, while the agent's adaptive planning improves coverage in high-risk zones. We also implement fairness-aware mechanisms to reduce bias in over-policed communities. This study offers one of the first fully integrated frameworks for real-time crime forecasting and patrol decision-making, setting the stage for aligned, adaptive, and ethically responsible AI in law enforcement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".