Colonel B. S. Nagial (Retd.) On 27 March 2023, United Nations Counter-Terrorism Executive Directorate (UNCTED) hosted an insight briefing on the blind spots in technology-driven counter-terrorism decision-making processes and proposed methods to mitigate these blind spots. UNCTED’s meeting focused on using predictive technologies to improve counter-terrorism initiatives, especially border security.1 One of the main takeaways of this briefing was that while predictive and probabilistic algorithms, human and signals intelligence, big data analytics, and facial recognition capabilities offer opportunities for countries’ efforts to address the scourge of terrorism, they also present many challenges.The United Nations Security Council (UNSC)’s guidance given out in its resolution 2396 (2017) on the assistance of biometrics in counter-terrorism and the necessity to enhance standards for using and collecting biometric data in counter-terrorism, the limitations in technology-driven counter-terrorism were outlined, and the suggestions for overcoming them have been elaborated therein. During his opening address, David Scharia, Director and Head of the Technical Expertise and Research Branch of UNCTED, said that the briefing was aimed at assisting countries to identify methods to upgrade technology-assisted decision-making processes in the context of counter-terrorism. This briefing featured a presentation from Professor Krebs, a Professor of Law at Deakin University, Australia, and a UNCTED’s Global Research Network member. The presentation was titled: Fact and Fiction in Technology-Driven Technology. This presentation elaborated on how counter-terrorism efforts in the airport and border security have gradually evolved towards preventative counter-terrorism. The benefit of predictive and probabilistic technologies lies in their ability to provide vast amounts of immediate, relevant information, process it, and identify connections and inconsistencies. However, Professor Krebs noted that attempts to prevent terrorist attacks by identifying suspicious individuals, including from data collected on terrorism watch lists and databases and from law enforcement cooperation, could also create false predictions about people and incorrectly assess the risk they pose. This could, in turn, negatively affect the principles of human rights, equality, and privacy, to name just a few. She further explained how technological limitations, limitations surrounding human use, and cognitive biases could cause decision-making errors in counter-terrorism risk assessments. She ended her presentation with a few suggestions for improving predictive counter-terrorism. Professor Krebs cited the need to develop transparent data practices and decision-assisting technologies, develop strengthened and clarified evidentiary standards, and provide capacity-building training to assist in de-biasing national and international decision-makers. Predictive technologies can be utilised to improve counter-terrorism initiatives in many ways. These technologies leverage such as data analysis, machine learning, and artificial intelligence to process and analyse large volumes of data, identify patterns, and make predictions that can help prevent, detect, and respond to terrorist activities effectively Here are some ways in which predictive technologies can be employed to improve counter-terrorism efforts: Early Warning Systems: Predictive technologies can analyse diverse data sources such as social media, communication networks, financial transactions, and travel patterns to identify potential warning signs of terrorist activities. By analysing these data in real-time, predictive technologies can help to identify suspicious activities or behaviours that may indicate the planning or execution of a terrorist attack. Early warning systems can provide timely alerts to law enforcement agencies, allowing them to take preventive measures and disrupt terrorist activities. Threat Assessment: Predictive technologies can analyse vast amounts of data to assess the threat level of individuals or groups suspected of being involved in terrorism. This can include analysing their social media posts, online activities, travel patterns, financial transactions, and other relevant data. By using machine learning algorithms, predictive technologies can identify patterns and indicators that may suggest the likelihood of an individual or group engaging in terrorist activities, helping law enforcement agencies prioritise their resources and focus on high-risk threats. Risk Prediction: Predictive technologies can use historical data and machine learning algorithms to predict the likelihood of specific locations or events being targeted by terrorists. By analysing patterns of past terrorist attacks, including location, timing, and modus operandi, predictive technologies can identify high-risk areas or events that may be vulnerable to terrorism. This information can help law enforcement agencies take preventive measures such as increased security measures, surveillance, and crowd management strategies to mitigate the risk of terrorist attacks. Social Media Monitoring: Predictive technologies can monitor social media platforms to identify and track individuals or groups promoting or inciting terrorism. By analysing social media posts, comments, and interactions, predictive technologies can detect patterns and keywords that may indicate radicalisation or recruitment activities. Social media monitoring can help law enforcement agencies identify and intervene with individuals vulnerable to radicalisation or engaging in online extremist activities. Border Security: Predictive technologies can be used to analyse data related to travel patterns, passports, visas, and other relevant information at border checkpoints. By leveraging machine learning algorithms and data analytics, predictive technologies can help identify potential terrorists or individuals with suspicious travel patterns, false documents, or other indicators of terrorist activities. This can help improve border security measures and prevent terrorists from entering or exiting a country. Resource Allocation: Predictive technologies can help optimise the allocation of limited resources, such as personnel, budget, and equipment, in counter-terrorism efforts. By analysing data on previous terrorist activities, response times, and resource utilisation, predictive technologies can help law enforcement agencies allocate their resources more effectively and efficiently. This can improve the overall operational readiness and effectiveness of counter-terrorism initiatives. However, it’s important to note that predictive technologies can provide valuable insights and support counter-terrorism efforts. But these technologies are not foolproof and must be used ethically and with appropriate legal safeguards to protect civil liberties, privacy, and human rights. Human oversight, accountability, and transparency should be maintained in using predictive technologies for counter-terrorism to ensure responsible and effective deployment. Challenges Associated With the Use of Predictive Technologies in Counter-terrorism. While predictive technologies in counter-terrorism could be promising, but presents several challenges. These challenges could summarise as under: Ethical concerns: Predictive technologies in counter-terrorism raise ethical concerns, such as bias, discrimination, and privacy. If trained on partial data, predictive technologies may be biased, leading to discriminatory outcomes, especially against certain…