i.
Predictive analytics for terrorist activities
The application of predictive analytics for counter-terrorism could, in some ways, be described as the “Holy Grail” for security forces, enabling them to transcend a traditionally reactionary approach to terrorism and become more proactive by anticipating future terrorist activities and intervening before an attack occurs. To allow for this, an AI model would need to be fed large quantities of real-time data regarding the behaviour of a terrorist or a suspected individual. By analyzing this data, such a model could potentially, for instance, make predictions regarding the likely future activities of these individuals. Given the massive growth over the past decade in the amount of data regarding individuals’ behaviour online, especially on social media, there has been growing interest in exploring how social media data collected regarding individuals’ behaviour online can be used to predict terrorist activities. Given the unpredictability of human behaviour and the current state of technological development, the application of algorithms to predict behaviour at an individual level is likely to remain of very limited value.64 Additionally, human rights experts and civil society organizations have pointed towards several ethical concerns regarding potential entry points for discriminatory judgement and treatment. The large quantities of data concerning an individual required for the algorithm to accurately function further give rise to concerns about the possibility of unwarranted mass surveillance. Predictive analytics can, however, still contribute to countering terrorism, but in a different manner. Rather than monitoring individuals online and forecasting their behaviour, predictive models informed by statistics from online sources that have been thoroughly anonymized or at least pseudonymized to protect user privacy could be used to identify trends or forecast the future behaviour of terrorists. This analysis based on aggregated data can be helpful to support security and intelligence agencies prioritizing scarce resources as operational support, making strategic decisions or providing warnings to the competent authorities. The examples below illustrate how predictive analytics create deep insights on the network structure of terrorist groups, predicting fragmentations and creating policies aimed at reducing attacks.
Photo by Kevin Ku on Unsplash INSIKT Intelligence, a tech start-up active in this domain, employs different machine learning models to detect potential threats online through NLP and SNA techniques performed on open-source content acquired from social media and other sources.65 The insights derived as a result of the text and network analysis are then used to identify potentially dangerous content and possible threats or prescribe patterns of relationships between individuals or organizations. Using SNA, INSIKT assesses the activity of a group of users in a network, determining nodes of influence and levels/effectiveness of information diffusion from, for example, propaganda across these networks. This open-source64
Alexander Babuta, Marion Oswald & Ardi Janjeva. (Apr. 2020). Artificial Intelligence and UK National Security: Policy Considerations. RUSI Occasional Papers. Accessible at https://static.rusi.org/ai_national_security_final_web_version.pdf.
65 See https://www.insiktintelligence.com/
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