Missile defense systems are a hot topic of discussion recently. While these highly advanced military technologies are essential to modern warfare, they possess inherent risks. The systems are designed to intercept and destroy incoming missiles, but what happens if the artificial intelligence (AI) or machine-learning (ML) models that assess collected data and make the decisions to take action fail to function properly or make the wrong decisions?
A missile defense system relies on a layered structure of space-, sea-, and ground-based radar, sensor arrays, and computer algorithms to detect and track incoming threats, before ultimately launching an interceptor missile that deploys as its end stage an explosive charge to detonate the enemy payload or a kill vehicle to destroy the incoming missile by colliding with it.
Using AI in missile defense systems has several strategic advantages. First, extremely sophisticated AI enables the ML models to analyze a vast amount of data in real time to identify and respond to threats rapidly, making the system highly effective against fast-moving targets such as cruise, ballistic, and hypersonic missiles.
Second, AI/ML models are key components of the tracking and guidance portion of the system, which enables critical and rapid predictions about the precise trajectory of incoming warheads. Knowing exactly when and where to launch interceptor missiles greatly increases the prospect of successful engagement with and destruction of enemy threats.
Finally, using AI reduces the workload on human operators, allowing them to focus on other tasks and achieve greater efficiencies.
While these are war-winning advantages, the risk of system failure exists when the AI/ML models relied upon do not perform as expected or required. For example, if models cannot accurately predict the trajectory of an incoming warhead, the ability of the system to intercept and destroy it is compromised. Additionally, environmental conditions can degrade the effectiveness of these systems, such as instances as ordinary as the local weather. Wind velocity and direction, for example, can affect missile trajectories, while heavy rain or fog can reduce the range and accuracy of sensors and other data collection measures, all of which make it more difficult to detect and accurately track targets. Models that have not been stress-tested for performance using data collected under such conditions can make skewed or inaccurate predictions, which result in unsound decisions.
Another key risk to mission success is AI bias, in which, unbeknownst to the end user, the AI/ML models deployed are limited in scope or scale, or were trained using poor-quality data. This, too, leads to skewed or inaccurate predictions that could, in turn, produce flawed decisions about where and when to launch interceptors, thus diminishing the system’s ability to protect against incoming threats.
While the use of carefully trained and tested AI/ML models is essential for a missile defense system’s effectiveness, it is equally essential that the models are monitored and maintained to ensure they execute properly and without bias in every instance. Such vigilance at the model level supports overall system robustness, guards against system failure, and assures continued protection against incoming threats.
