Predictive Technologies and CounterTerrorism

Colonel B. S. Nagial (Retd.)

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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:

1. 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 groups or communities. Furthermore, using predictive technologies in counter-terrorism may raise privacy concerns. It often involves extensive data collection and analysis, including the personal information of individuals who may not be involved in terrorist activities.
2. Legal and regulatory issues: Legal and regulatory challenges are associated with using predictive technologies in counter-terrorism. Using predictive technologies to decide individuals’ rights and liberties such as identifying potential terrorists or determining threat levels, may raise questions about due process, accountability, and transparency. There may also be concerns about the legality of data collection and surveillance practices associated with predictive technologies in counter-terrorism, particularly regarding cross-border data sharing and international legal frameworks.
3. Accuracy and reliability: The accuracy and reliability of predictive technologies in counter-terrorism are critical concerns. Predictive Technologies rely mainly on data for training, and the quality and completeness of the data can significantly impact the accuracy of predictions. Incomplete or biased data can lead to incorrect predictions, false positives, or false negatives, which may result in ineffective or harmful actions. Additionally, the rapidly changing nature of terrorism and evolving tactics used by terrorists may make it challenging for predictive technologies to keep up with the dynamic nature of the threat landscape.
4. Human oversight and accountability: Using predictive technologies in counter-terrorism raise questions about human oversight and accountability. While these technologies can assist in identifying potential threats and patterns that may be difficult for humans to detect, human judgment and decision-making are still crucial. Human operators must have the ability to understand and interpret the outputs of predictive technologies, and they must be responsible for making final decisions based on those outputs. Ensuring proper human oversight and accountability for predictive technologies in counter-terrorism is essential to avoid potential errors, biases, or unintended consequences.
5. Trust and public perception: Trust and public perception are crucial for effectively using predictive technologies in counter-terrorism. The use of predictive technologies in counter-terrorism may raise concerns among the public about surveillance, invasion of privacy, and potential abuse of power. Suppose people do not trust the technology or the entities using it. In that case, they may be less likely to cooperate or provide the necessary information, which could hinder the effectiveness of counter-terrorism efforts. Building trust and maintaining public confidence in using predictive technologies in counter-terrorism is essential to ensure its successful implementation.
6. Adaptability and agility: Terrorism is a constantly evolving threat, with terrorists often adapting their tactics and techniques. Predictive technologies must be agile and adaptable to keep up with these changes. Regular updates and improvements to the algorithms and models used in predictive technologies are necessary to ensure their accuracy and effectiveness in the face of evolving threats. However, implementing changes in predictive technologies may also pose challenges regarding cost, technical expertise, and integration with existing infrastructure.

Conclusion

While predictive technologies have the potential to enhance counter-terrorism efforts, but also present significant challenges. Ethical concerns, legal and regulatory issues, accuracy and reliability, human oversight and accountability, trust and public perception, and adaptability and agility are critical challenges associated with the current use of predictive AI in counter-terrorism. Addressing these challenges is crucial to ensure that predictive technologies are responsible and effective in counter-terrorism efforts while upholding human rights, privacy, and societal values.

1. CTED Insight Briefing brings technology-driven counter. https://www.un.org ‘ securitycouncil ‘ ctc ‘ news. Assessed on 11 April 2023.
2. IBID

*Views expressed in the article are solely of the Author

---