A technique used in image analysis that has proven very good at recognizing faces under difficult conditions. (sw. Convolutional neural networks)
Published in Chapter:
Harnessing the Power of Artificial Intelligence in Law Enforcement: A Comprehensive Review of Opportunities and Ethical Challenges
Akash Bag (Amity University, India),
Souvik Roy (Adamas University, India), and Ashutosh Pandey (Adamas University, India)
Copyright: © 2024
|Pages: 25
DOI: 10.4018/979-8-3693-1565-1.ch008
Abstract
Law enforcement is joining the fast-growing artificial intelligence (AI) research field. The chapter tries to fix that. This chapter utilized a “systematic literature review.” The authors gathered research papers on using algorithms and AI in police work. This was done with Scopus, a fancy academic database. They searched for papers on “law enforcement,” “policing,” “crime prevention,” “crime reduction,” and “surveillance.” Combine these terms with “algorithm” or “artificial intelligence.” They found that AI has great potential to aid law enforcement. It can recognize faces, forecast crimes, and track people. These AI tools usually analyze photos, behavior, language, or a combination. However, there are significant “but” ethical issues that exist. AI can cause unjust treatment, confusion about responsibility, oversurveillance, and privacy invasion. AI's benefits and cool abilities are often highlighted over its drawbacks. Another observation is that writings on the same topics agree on what AI can achieve, its potential, and what we should explore next.