From Human-in-the-Loop to Accountability Gaps: AI-Enabled Targeting Tools and International Humanitarian Law

Abstract

Recent military use of machine learning tools for target identification has raised concerns about how these systems affect compliance with International Humanitarian Law (IHL). Decision support systems such as Israel's Lavender have shown that operators may rely too heavily on algorithmic recommendations, especially during time pressure. This reliance can speed up strike decisions while making it harder to trace the source of errors. IHL rules on distinction, proportionality, necessity and precaution were developed for human decision makers who can understand and question the basis of an attack. However, contemporary targeting software produces recommendations that are difficult to audit and may reflect hidden biases in training data. This article highlights four gaps in current scholarship: limited analysis of non-autonomous decision support tools, the lack of behavioural research on automation bias in military contexts, weak transparency in national Article 36 weapons reviews and insufficient attention to technical safeguards that could prevent errors before they occur. The study proposes a combined legal and socio-technical approach to examine how these systems function across their entire life cycle. It evaluates their practical effects on IHL compliance and outlines reforms in doctrine, review processes and system design. The goal is to ensure clearer accountability and stronger protection for civilians as algorithmic tools become a routine part of modern warfare.