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AI can help military personnel analyze information or recommend targets, but it can also misread data, miss context, be manipulated, and accelerate decisions until human scrutiny becomes less meaningful. These risks matter because attacks must comply with international humanitarian law: people remain responsible for distinguishing military objectives from civilians, assessing expected civilian harm, and taking feasible precautions. Using AI does not transfer those obligations to software.
What “AI in military targeting” can mean
The phrase covers different functions, and the function matters. Some systems analyze information or support a person’s decision. Others may perform critical functions in selecting and attacking targets. Surveillance, navigation, or flight functions do not, by themselves, mean that a weapon autonomously selects and attacks a target. The International Committee of the Red Cross (ICRC) draws this distinction when discussing autonomy in weapons and other military uses of AI.
That difference is important when evaluating risk. An analytical tool can influence a decision without making it; an autonomous weapon may perform part of the selection-and-attack process itself. In either case, AI involvement alone does not establish that a system is inaccurate, unlawful, or autonomous. A meaningful assessment asks what the system actually does, what decisions people make, and whether those people can exercise sound judgment in the circumstances.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11| Use of AI | Role in the targeting process | Key question |
|---|---|---|
| Analysis or decision support | Processes information or advises a human decision-maker; the person is expected to decide what to do. | Can the operator understand, scrutinize, and reject the output in time? |
| Autonomous critical functions | The system performs critical functions in selecting and attacking targets. | What limits apply to target selection and attack, and what meaningful human control remains? |
| Other military functions | AI may support activities such as surveillance, navigation, or flight without itself selecting and attacking targets. | How does this function affect the information or conditions on which a human targeting decision depends? |
The ICRC’s military AI questions and answers and its human-centred analysis of AI in armed conflict explain why these distinctions matter. Labels such as “AI targeting” are not enough to tell a reader which function a system performs.
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How AI can contribute to targeting errors
Unreliable or incomplete data
An AI system’s output depends on the information it receives. If the underlying data are unreliable, incomplete, or otherwise unsuitable for the decision, analysis based on that data may also be unreliable. The UN report A/79/224 (2024) identifies data reliability as a risk to distinction. This is not a claim that every military AI system uses bad data; it is a reason to consider the quality and limitations of the information used in a specific application.
Pattern-recognition errors
AI may identify a pattern incorrectly. In a targeting context, a mistaken classification can contribute to confusing a military objective with a civilian person or object. Pattern recognition is not the same as a legal determination that an attack is permissible: the system’s apparent match does not settle what the object is, what role it has in the circumstances, or whether an attack may lawfully proceed.
Missing context
A system can process signals or patterns without understanding the surrounding circumstances in the way a human decision-maker must. The UN report identifies lack of contextual understanding alongside data reliability and pattern-recognition errors as concerns for distinction. Context matters because a targeting decision is not made correctly just by matching a data point to a category; it requires assessing the actual circumstances of an attack.
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These are mechanisms of risk, not a universal error rate. The sources cited here do not establish a comparable, verified real-world statistic for how often AI-assisted military targeting systems misidentify a target. A number without a defined system, population, period, and measurement method would give a false impression of precision.
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Why human approval may not amount to meaningful judgment
A person may formally approve a recommendation yet have little practical opportunity to assess it. If the system’s reasoning is opaque, the decision arrives too quickly, or the operator over-trusts the output, nominal human involvement may not provide meaningful scrutiny. This concern is often described as automation bias: people can give a system’s recommendation more weight than they should.
The U.S.-endorsed Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy calls for training users to understand system capabilities and limitations and to mitigate automation bias. It also calls for transparent and auditable methods, data sources, and design procedures. These measures address how people interact with a system; they do not establish that every system is transparent or that every operator can independently verify each output.
Time and authority matter as much as a human’s presence. To exercise judgment, a responsible person needs enough information and time to assess a recommendation, the authority to reject or alter it, and the ability to intervene when circumstances change. If those conditions are absent, a human checkpoint may offer less protection than its label suggests.
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How adversaries can manipulate AI
AI systems may be deliberately deceived. An opponent could tamper with data or exploit weaknesses to induce a system to behave incorrectly. The ICRC identifies this vulnerability to deception and manipulation as a concern, particularly in adversarial environments. A system that performs as expected under ordinary testing may not behave the same way when an adversary is actively trying to confuse it.
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This makes robustness a question of operating conditions, not just a general claim that a system has been tested. Relevant scrutiny includes whether testing reflects realistic conditions, whether data problems and adversarial manipulation have been considered, and whether the system can be monitored for unintended behavior. The available sources identify these concerns and policy expectations; they do not provide a common performance score for deployed targeting systems.
Why scale and speed raise separate legal and humanitarian risks
Large-scale targeting and civilian harm
AI may enable targeting processes to operate at a scale that makes context-specific assessment harder. The UN report A/79/224 warns that large-scale targeting applications may challenge proportionality and precautions. Proportionality requires assessing whether expected civilian harm would be excessive in relation to the anticipated military advantage. Precautions require taking feasible steps to avoid or minimize civilian harm. The report identifies a challenge to applying these rules at scale; it does not say every AI use is indiscriminate or unlawful.
The concern is that a process can produce or act on many recommendations faster than people can assess the circumstances of each attack. If the decision process does not preserve careful evaluation of expected harm and feasible precautions, the ability to process more information or more targets does not make the resulting decisions legally sound.
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Faster processing can pressure decision-makers to act with less time for scrutiny. The ICRC warns that AI may accelerate the pace and scale of warfare and reduce human control. Separately, the UN Secretary-General and ICRC president have warned that a perceived reduction in the risk of engaging in conflict could lower the threshold for doing so and inadvertently escalate violence.
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In their October 5, 2023 joint statement, António Guterres, UN Secretary-General, and Mirjana Spoljaric, ICRC president, said: “The autonomous targeting of humans by machines is a moral line that we must not cross.” Their statement links the concern not only to an individual targeting decision but also to how autonomous weapons could affect the decision to enter or expand a conflict. The warning about escalation is a risk, not a claim that AI has inevitably caused a particular conflict to escalate.
What international law requires
International humanitarian law applies to military uses of AI. In an attack, the relevant obligations include distinction, proportionality, and precautions. Distinction requires distinguishing military objectives from civilians and civilian objects. Proportionality concerns whether expected incidental civilian harm would be excessive in relation to the anticipated military advantage. Precautions require feasible measures to avoid or minimize harm.
These judgments depend on the circumstances of an attack. The ICRC’s position is that they cannot be transferred to software or an algorithm. A system may inform a human decision, but its output does not itself satisfy the legal obligations or assume responsibility for the attack. The ICRC discusses this human-centred approach in its analysis of AI and machine learning in armed conflict.
U.S. policy provides one example of national guidance, not a substitute for the international rules. On January 25, 2023, the U.S. Department of Defense announced an update to Directive 3000.09. It says autonomous and semi-autonomous weapon systems should allow commanders and operators appropriate human judgment over the use of force. It also says personnel authorizing, directing, or operating these systems must exercise appropriate care and comply with applicable law, treaties, safety rules, and rules of engagement. Those are policy requirements; their existence alone does not show that all systems meet them in practice.
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What safeguards can reduce risk—and what they cannot prove
Safeguards are ways to manage risk, not proof that it has disappeared. The U.S. Department of Defense’s summary of the Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy identifies measures concerning oversight, bias, auditability, training, rigorous testing and assurance, and safeguards for unintended behavior. It reported on November 22, 2023, that 47 states had endorsed the declaration. That is an endorsement count for that date, not a compliance rate or evidence that the risks have been solved.
The Department of Defense’s November 22, 2023 summary of the declaration and its January 25, 2023 announcement on Directive 3000.09 describe policy measures and expectations. For readers assessing what those measures mean in practice, the useful questions are specific:
- Function: Does the system analyze information or advise a person, or does it perform critical functions in selecting and attacking targets?
- Legal judgment: Can people apply distinction, proportionality, and precautions to the actual circumstances of the attack?
- Testing: Has performance been tested under realistic conditions, including data problems and adversarial manipulation?
- Human control: Do responsible people have the time, understanding, authority, and ability to intervene or reject the system’s output?
- Auditability: Can the methods and data sources be examined, and can people understand the system’s limitations?
- Unintended behavior: Are there safeguards to disengage or deactivate a system if it behaves in an unintended way?
These checks do not establish that a particular military system is safe, lawful, or effective. They help distinguish a concrete account of oversight and testing from a broad assurance that a system is responsible. The sources cited here do not support ranking named deployed targeting systems against one another.
How to interpret claims about AI targeting
When a government, military, or other source describes an AI-enabled targeting capability, first establish what “AI” does in that system. Ask whether it analyzes information, recommends a decision, or performs critical selection-and-attack functions. Then look for evidence about the data and operating conditions, how operators are trained, what authority they retain, and whether they can interrupt the process. Finally, ask how the specific attack decision preserves the legal assessments of distinction, proportionality, and precautions.
Be cautious with claims that AI is inherently precise or inherently inaccurate. The cited UN and ICRC materials identify risks and mechanisms, but the available evidence here does not provide a comparable real-world error-rate statistic or a basis for ranking deployed systems. Likewise, declarations and directives describe policy commitments and expectations; they should not be mistaken for proof of system-level compliance.
Quick Recap
Sources and policy references
- ICRC, “Frequently asked questions: Artificial Intelligence (AI) in the military domain,” June 11, 2026.
- ICRC / International Review of the Red Cross, “Artificial intelligence and machine learning in armed conflict: A human-centred approach”.
- United Nations Digital Library, A/79/224, 2024.
- United Nations, joint call by the UN Secretary-General and ICRC president on autonomous weapon systems, October 5, 2023.
- U.S. Department of Defense, update to Directive 3000.09 announcement, January 25, 2023.
- U.S. Department of Defense, responsible AI measures and declaration endorsement count, November 22, 2023.
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