Designing for Reasonableness: the Algorithmic Mediation of Reasonableness in Targeting Decisions

Developments on current battlefields are proving that algorithmic decision-support systems are not a distant future, but a reality that military and civilian populations are already living with. For example, in November 2023, it was reported that defense contractor Palantir has provided their artificial intelligence platform, or AIP, to the Ukrainian military. AIP is a system powered by machine learning and connected to large language models that offers a chat box function. It is used to identify areas with mines and support demining efforts. Recent reports have also revealed the use of algorithmic decision-support systems for targeting by the Israel Defense Forces (see here and here). The system called Gospel is used to identify in real-time the location and movement of suspected Hamas operatives.

These and other decision-support systems are using machine-learning algorithms to find patterns in large databases of sensor data, satellite photos and drone footage, and even social media feeds in order to identify, locate, and track the movements of suspected targets. They can also be used to estimate collateral damage and provide recommendations for courses of action. The speed at which such systems process information and provide target sites is unprecedented; it is faster than any previous human efforts and the actual capacity to strike. With these characteristics, many are concerned with the impact such systems will have on the legal obligations of commanders in relation to targeting decisions in conflicts.

The purpose of this post is to raise awareness of the use of algorithmic tools by military actors and to argue that algorithmic decision-support systems significantly reshape the reasonableness of military decisions.