Artificial intelligence (AI) has entered a new generation that focuses on counterterrorism strategies, thereby altering the processes of detection, prevention, and response to terrorist threats as followed by the governments and security interfacing agencies. With AI-powered technologies like machine learning, predictive analytics, and excellent surveillance systems, there is the added capability of dealing with vast amounts of data in near real time. This, in turn, identifies the potential threats and detection of suspicious activity patterns and facilitates the governments in timely and accurate analysis. AI-enabled instruments, such as unmanned aerial vehicles (UAVs), facial recognition biometrics programs, and data-mining applications, add to the effectiveness in the realms of intelligence acquisition, targeting of suspects, and operational efficacy in counterterrorism. The related issue of algorithmic bias presents ethical quandaries. AI systems are only as trustworthy as the data they are trained on, and any biases present in such datasets, be they on the basis of race, religion, or socio-economic background, can lead to disproportionate targeting of specific communities. Intentionally or unintentionally, these biases can also strengthen existing prejudices, aggravate social divides, and contribute to wrongful actions against the counterterrorism enforcement. On a parallel, AI-specific mass surveillance tools trigger the privacy versus civil liberties argument. The more mass data subjected to collection and scrutiny, the greater the potential infringement on individual freedoms and the greater the concern, particularly with the use of coercive means lacking in law. This great concern applies particularly to already marginalised communities that could be disproportionately impacted by AI-empowered surveillance systems and data-monitoring systems.