As algorithms begin to produce decisions that determine who lives and who dies on the battlefield, the rise of AI-driven autonomous weapon systems (AWS) is forcing a re-examination of some of the most basic principles of international humanitarian law (IHL). This is especially so as recent breakthroughs in AI have fueled AWS proliferation among major powers, with China and Russia in particular pouring increasing resources into innovations like swarming drones. Despite supporters pointing to AWS’s potential for increased precision , decreased human error , and fewer military casualties , its introduction into armed conflicts raises pressing questions about compliance with IHL and the capacity to hold parties responsible for ensuing harms.
Recognized as one of the ideal IT Management Products in the 2026 G2 Leading Software Awards
Explainable AI (XAI) addresses this imperative by making the reasoning behind AI outputs transparent and interpretable. Without such transparency, societies risk delegating consequential decisions to opaque systems that cannot be questioned, audited, or meaningfully understood.
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement , Privacy Policy , and Cookie Policy .
Explainable AI refers to methods and system designs that enable humans to understand how and why an AI model arrives at a particular output. Numerous advanced AI systems, particularly those based on deep neural networks, are often described as “black boxes.” They produce highly accurate predictions, yet their internal logic is difficult to interpret even for experts.
Take mines for example. They are among the oldest autonomous weapons, operating via simple stimulus-response mechanisms (e.g., pressure or magnetic triggers) without human oversight, yet are subject to stringent regulations. The 1997 Ottawa Convention prohibits anti-personnel mines because they are indiscriminate, meaning they cannot distinguish between civilians and combatants and continue to kill thousands long after conflicts are over . Anti-vehicle mines are still permitted on the battlefield, but only if they actually follow IHL rules, although the example of the Russo-Ukrainian War shows how easily rules can be disregarded.
Explainable AI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production. AI explainability additionally helps an organization adopt a responsible approach to AI development.
Artificial intelligence is embedded in the infrastructure of modern life. From financial approvals and medical diagnostics to hiring platforms and workplace productivity tools, AI systems increasingly shape decisions that affect individuals and institutions alike. As reliance deepens, a strategic imperative emerges: AI systems must not only be powerful, but understandable. In an era marked by algorithmic misinformation, automated decision-making, and widespread dependence on AI for everyday work, explainability is essential for trust, accountability, and responsible governance.
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement , Privacy Policy , and Cookie Policy .
Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms.
