TY - JOUR
T1 - Understanding Patterns of Terrorism in India (2007-2017) Using Artificial Intelligence Machine Learning
AU - Verma, Dinesh
AU - Yarlagadda, Rithvik
AU - Gartner, Scott Sigmund
AU - Felmlee, Diane
N1 - Funding Information:
This research was sponsored by the US Army Research Laboratory and the UK Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the US Army Research Laboratory, the US Government, the UK Ministry of Defence, or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copy-right notation hereon. Funding was also provided by The Pennsylvania State University Center for Security Research and Education and The Pennsylvania State University Criminal Justice Research Center. An early version of this article was presented at The Fifteenth International Conference on Technology, Knowledge and Society, Barcelona Spain March 12, 2019. The authors would like to thank conference participants for their helpful feedback.
Publisher Copyright:
© 2019 Common Ground Research Networks. All rights reserved.
PY - 2019
Y1 - 2019
N2 - With the tremendous increases in Artificial Intelligence (AI) computing technology capabilities, applications of AI approaches to terrorist data can yield useful insights into the interaction of terrorists, governance, and geography. There have been few applications of machine learning techniques to understand patterns of terrorist behavior. Specifically, little work has been done to analyze terrorism patterns in India, which experiences one of the world's highest levels of terrorism. We apply "shallow AI models" to a decade of terrorist incidents in India. We show that AI approaches generate highly accurate models that predict levels of violent incident behavior across locations from a history of past attacks, and identify the principal factors correlated with a location being targeted. This study provides an example of socially-relevant AI research, expands our understanding of the dynamics of terrorism in a way that can help to shape counterterrorism policy and contributes to our greater recognition of the interwoven relationship of technology, knowledge, and society.
AB - With the tremendous increases in Artificial Intelligence (AI) computing technology capabilities, applications of AI approaches to terrorist data can yield useful insights into the interaction of terrorists, governance, and geography. There have been few applications of machine learning techniques to understand patterns of terrorist behavior. Specifically, little work has been done to analyze terrorism patterns in India, which experiences one of the world's highest levels of terrorism. We apply "shallow AI models" to a decade of terrorist incidents in India. We show that AI approaches generate highly accurate models that predict levels of violent incident behavior across locations from a history of past attacks, and identify the principal factors correlated with a location being targeted. This study provides an example of socially-relevant AI research, expands our understanding of the dynamics of terrorism in a way that can help to shape counterterrorism policy and contributes to our greater recognition of the interwoven relationship of technology, knowledge, and society.
UR - http://www.scopus.com/inward/record.url?scp=85089173899&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089173899&partnerID=8YFLogxK
U2 - 10.18848/1832-3669/CGP/v15i04/23-39
DO - 10.18848/1832-3669/CGP/v15i04/23-39
M3 - Article
AN - SCOPUS:85089173899
SN - 1832-3669
VL - 15
SP - 23
EP - 39
JO - International Journal of Technology, Knowledge and Society
JF - International Journal of Technology, Knowledge and Society
IS - 4
ER -
