Research Rooted in Machine Learning Challenges Conventional Thinking About the Pathways to Violent Extremism

• NCJ Number: 307130
• Date Published: July 2023
• Grant Number(s): 2017-ZA-CX-0002
• Agencies: NIJ
• Publication Type: Research (Applied/Empirical)
• Award: Dynamic, Graph-Based Risk Assessments for the Detection of Violent Extremist Radicalization Trajectories Using Large Scale Social and Behavioral Data

Annotation

This article reports on a research project that examines youth's motivations for engaging in violent extremism, aimed at developing a new analytical method that uses machine learning; the article discusses key insights from the research findings, research limitations, and implications for public policy.

Abstract

Researchers have developed a new analytical method to better understand how individuals move toward violent extremism. Using machine learning, a form of artificial intelligence, the method reveals clusters of traits associated with possible pathways to terrorist acts. The resource may improve our understanding of how an individual becomes radicalized toward extremist violence. A National Institute of Justice-supported scientific study that deploys those tools and blends elements of data science, sociology, and criminology is calling into question some common assumptions about violent extremism and the homegrown individuals who are motivated to engage in behaviors supporting violent jihadist ideologies. This article describes how NIJ-funded research looking at large datasets challenged some long-accepted beliefs on what motivates young people to engage in violent extremism.