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Machine Learning and the Prevention of Mass Shooting in the United States

NCJ Number
309746
Author(s)
Arish Madataly; Benedictus Filbert Federico; Daniel Eric Phangandy; Nunung Nurul Qomariyah; Raymond Bahana
Date Published
September 2023
Length
9 pages
Annotation

In this project, the authors use machine learning to study the relation between a person’s life events and the chances that person will commit a mass shooting.

Abstract

In this study, researchers seek to find the relation between a person's life events and their likelihood to commit a mass shooting using machine learning. Researchers were able to see an emerging pattern between the offenders' life factors. Certain trauma events and mental illnesses were present in the majority of the lives of the people studied. The data in fact proved that mental illnesses or traumas on their own are not sufficient information for the prediction but their combination allows more meaningful results. Hence, the results suggest that a high presence of mental instabilities can increase the likelihood of someone becoming a mass shooter. Mass shootings have been a rising issue for decades now and have started rising again in the USA lately. Not only do they take the lives of people, but they also create anxiety for others. Data shows that they even increase people's fear of going to public places. Research carried out by psychologist Jillian Peterson and sociologist James Densley called the Violence Project investigates the personal life of individuals that committed mass shootings. They recorded all the data into an open and updated database for use in research. At the time of this research, it had the data of 181 mass shooting events that took place from 1966 to 2022 in the United States of America. (Published Abstract Provided)