From Peril to Promise: Harnessing Machine Learning and Natural Language Processing for Combating Privately Manufactured Firearms in the United States
DOI:
https://doi.org/10.58445/rars.1172Keywords:
Artificial intelligence, machine learning, neural networks, privately manufactured firearmsAbstract
This paper will serve as a review of current literature about using artificial intelligence, including but not limited to deep neural networks, machine-learning, and computer vision, in detection of privately manufactured firearms. This paper will review detection of privately manufactured firearms in the digital environment (e.g. the Web) and in the physical world (e.g. surveillance images and videos). Further, the hopes to illuminate how current forensic and crime analysis applied to traditional firearms are not transferable to privately manufactured firearms (PMFs), thus necessitating application of artificial intelligence methods that are currently used in traditional crime analysis and firearms regulation. Though there are few publications about using machine learning methods for identification of privately manufactured firearms, this literature hopes to change that by offering recommendations on how to apply machine learning techniques to the issue of PMFs. This paper advocates for the application of machine learning and computer vision techniques to identify and classify privately manufactured firearms, addressing a critical gap in current forensic methods. By leveraging advanced AI technologies, we aim to enhance surveillance and intervention strategies to combat the evolving challenges of gun violence.
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