Decoding Justice: The Synergy of Artificial Intelligence and Machine Learning in the Legal Landscape

The rapid integration of Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies holds significant promise for enhancing human-centric applications, particularly in the domain of law enforcement. This paper explores the application of AI and ML in crime prevention and resource allocation, pivotal areas of concern for law enforcement agencies (LEAs) globally. By utilizing historical crime data, sensory inputs, and advanced analytics, this study contributes to the evolving discourse on smart policing and proactive crime strategies. Our objective is to facilitate the transformation of LEAs from primarily reactive entities into proactive crime preventers through the adoption of predictive analytics, facial recognition enhanced surveillance, and natural language processing for efficient data analysis. We emphasize that collaborative efforts with AI experts ensure the responsible use of technology, meticulously balancing security imperatives with privacy concerns.

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Publication Details

Published
2024-11-19
DOI
https://doi.org/10.1109/gcaiot63427.2024.10833589
Citations
1
Primary Topic
Digital Transformation in Law
Type
article
Field-Weighted Citation Impact
0.83
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article

Decoding Justice: The Synergy of Artificial Intelligence and Machine Learning in the Legal Landscape

Nadine Y. Fares, Manar Jammal
1 citations
Digital Transformation in Law
0.83
article

Decoding Justice: The Synergy of Artificial Intelligence and Machine Learning in the Legal Landscape

Nadine Y. Fares, Manar Jammal
article en
1 citations

Abstract

The rapid integration of Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies holds significant promise for enhancing human-centric applications, particularly in the domain of law enforcement. This paper explores the application of AI and ML in crime prevention and resource allocation, pivotal areas of concern for law enforcement agencies (LEAs) globally. By utilizing historical crime data, sensory inputs, and advanced analytics, this study contributes to the evolving discourse on smart policing and proactive crime strategies. Our objective is to facilitate the transformation of LEAs from primarily reactive entities into proactive crime preventers through the adoption of predictive analytics, facial recognition enhanced surveillance, and natural language processing for efficient data analysis. We emphasize that collaborative efforts with AI experts ensure the responsible use of technology, meticulously balancing security imperatives with privacy concerns.

York University (CA)
Peace, Justice and strong institutions
Openalex Percentile: Top 19%
Digital Transformation in Law
0.83
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Decoding Justice: The Synergy of Artificial Intelligence and Machine Learning in the Legal Landscape — Nadine Y. Fares, Manar Jammal · (2024) | TGRS Research Map | TGRS