Understanding Patterns of Terrorism in India Using AI Machine Learning: 2007-2017

Abstract Terrorism represents an undesirable but seemingly inevitable part of the modern social landscape and understanding terrorism dynamics can provide useful insights for developing governance structures and policies that are both more effective at reducing violence and less invasive on general society. With the tremendous increases that are happening in Artificial Intelligence capabilities in computing technology, application of AI technologies to terrorist data can yield useful insights regarding the interaction of terrorists, governance, and society. Generally, there have been few applications of machine learning techniques to understanding patterns of terrorist behavior. Specifically, little work has been done to use AI to analyze terrorism patterns in India, which experiences among the world's highest levels of terrorism. Using the Global Terrorism Database and the South Asian Terrorism Portal we apply "shallow machine learning models" that require only a modest amount of data to train themselves and can facilitate our exploration of three questions crucial to understanding the complex dynamics of terrorism, state and society: From a description of the attack can we identify the likely terrorist group? Can we predict the likely location for the next attack from a history of past attacks? Can we identify the principal factors that cause a city to be targeted? We believe that this project will: provide an example of socially-relevant AI research; expand our understanding of the factors that shape counterterrorism policy and contribute to our greater recognition of the interwoven relationship of technology, knowledge, and society.
  • Scott Sigmund Gartner (PSU)
  • Diane Felmlee (PSU)
  • Rithvik Yarlagadda (PSU)
  • Dinesh Verma (IBM US)
Date Mar-2019
Venue Fifteenth International Conference on Technology, Knowledge & Society, Barcelona Spain