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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAI can help military personnel analyze information or recommend targets, but it can also misread data, miss context, be manipulated, and accelerate decisions faster than people can properly scrutinize them. These failures matter because the laws of war require attacks to distinguish military objectives from civilians, weigh expected civilian harm against anticipated military advantage, and take feasible precautions. Using AI does not transfer those duties or human responsibility to a machine.
What “AI in military targeting” can mean
The phrase covers different functions, and the distinction matters. One system might analyze information and present a recommendation to a person. Another might carry out critical functions in selecting and attacking targets. Calling both “AI targeting” can obscure how much authority the system has and where people can intervene.
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The International Committee of the Red Cross (ICRC) distinguishes autonomy in the critical functions of selecting and attacking targets from AI uses such as surveillance, navigation, or flight. An AI-assisted analysis or recommendation therefore does not, by itself, mean that a weapon independently selects and attacks a target. The relevant question is what the system actually does—not whether it is described broadly as AI. See the ICRC’s FAQ on AI in the military domain and its discussion of a human-centred approach to AI and machine learning in armed conflict.
| Use of AI | What it does | Key targeting question |
|---|---|---|
| Analysis or decision support | Processes information or provides a recommendation for a person to consider. | Can the responsible person examine the underlying information, understand the system’s limitations, and make an independent decision in the actual circumstances? |
| Autonomy in critical weapon functions | Performs critical functions involved in selecting and attacking targets. | What human judgment and control remain over the use of force, and how does the system behave in the relevant conditions? |
| Other military uses | Supports functions such as surveillance, navigation, or flight. | Does the use affect a targeting decision, and if so, where does that effect enter the decision process? |
This is a functional distinction, not a ranking of safety. A decision-support tool can still shape a consequential choice; a system used for another function can still affect what information reaches a decision-maker. Understanding the path from input to action is essential to assessing the risk.
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How errors can lead to civilian harm
Military targeting requires judgments about people, objects, and circumstances. AI systems can make those judgments harder when their inputs are unreliable, their pattern recognition is wrong, or they fail to account for context. The UN report A/79/224 (2024) identifies data reliability problems, pattern-recognition errors, and lack of contextual understanding as risks to distinguishing military objectives from civilians and civilian objects.
Unreliable or incomplete inputs
An analysis system can only work with the information it receives. If relevant data are missing, inaccurate, outdated, or otherwise unreliable, an output may not reflect the circumstances on the ground. A confident-looking recommendation does not establish that its inputs are sound. People responsible for a decision need to know what information the system used and what it could not see.
Pattern recognition mistaken for understanding
Recognizing a pattern is not the same as understanding what a person or object means in a particular situation. A system might classify information in a way that seems relevant while failing to account for the circumstances that determine whether an attack is lawful. The UN report identifies pattern-recognition errors and lack of contextual understanding as distinct concerns; neither can be resolved simply by treating an output as a fact.
These are risk mechanisms, not proof that every AI system is inaccurate. The sources cited here do not establish a comparable, verified real-world error rate for AI-assisted military targeting. A numerical rate without a defined system, population, period, and measurement method would therefore give a false impression of precision.
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Why context-specific legal judgment matters
International humanitarian law applies to military uses of AI. In an attack, the relevant requirements include distinction, proportionality, and precautions. They concern the circumstances of a particular attack, not merely whether a system performed as designed.
- Distinction: Those planning or carrying out an attack must distinguish military objectives from civilians and civilian objects.
- Proportionality: The expected incidental civilian harm must be assessed in relation to the anticipated military advantage.
- Precautions: Feasible precautions must be taken in the circumstances to avoid or minimize civilian harm.
These principles are not a checklist that software can complete without human judgment. The ICRC says that the judgments needed to apply international humanitarian law cannot be transferred to a machine, software, or algorithm. The system may supply information or recommendations, but people remain responsible for making the legally required assessments in context. The ICRC’s human-centred analysis explains this position.
That distinction also prevents a common mistake: equating a system’s technical classification with a legal conclusion. An output about what a system has detected does not by itself settle whether an object is a military objective, what civilian harm is expected, or which precautions are feasible. Those questions depend on circumstances the output may not capture.
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How automation can weaken meaningful human judgment
A person’s name on an approval step does not guarantee that the person exercised meaningful judgment. If an operator cannot understand the recommendation, has too little time to scrutinize it, or treats it as authoritative, human involvement may be nominal rather than effective. This is especially important when a system’s methods or data sources are difficult to examine.
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The U.S.-endorsed Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy calls for training users to understand systems’ capabilities and limitations and to mitigate automation bias—the tendency to over-trust an automated output. It also calls for transparent and auditable methods, data sources, and design procedures. These measures address the conditions under which human oversight can become weak; they do not establish that every system or decision process meets them. The U.S. Department of Defense summarized the declaration’s measures in its November 22, 2023 account.
For human review to matter, the responsible person needs enough information to understand the recommendation, enough time to assess it, and real authority to reject or change the proposed action. A process that provides only a rapid output and a button to approve it does not, on that basis alone, demonstrate meaningful judgment.
How adversaries can exploit weaknesses
AI systems may encounter deliberate deception as well as ordinary data problems. An opponent could try to tamper with inputs, mislead a system, or exploit a weakness. The ICRC identifies manipulation and adversarial environments as concerns for military AI. Testing under ordinary conditions does not, by itself, show how a system will perform when the environment or inputs are contested.
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Why speed and scale can magnify consequences
Faster processing can compress the time available for people to check information, consider alternatives, and apply legal judgment. If AI also makes it possible to analyze or act on information at greater scale, mistakes or weak review can affect more decisions. The UN report identifies concerns about large-scale targeting applications and their implications for proportionality and precautions; this is a warning about risk, not a claim that every AI use is indiscriminate.
The ICRC warns that AI may accelerate the pace and scale of warfare and reduce human control. Faster decisions can create pressure to act before a person has adequately evaluated a recommendation. Scale can make it harder to ensure that context-specific assessments are actually being performed rather than assumed from a generalized system output.
There is also a risk beyond any single targeting decision: perceived reductions in the risk of using force could lower the threshold for engaging in conflict. In a joint statement on October 5, 2023, UN Secretary-General António Guterres and ICRC President Mirjana Spoljaric warned that autonomous weapons could contribute to that dynamic and inadvertently escalate violence. They stated: “The autonomous targeting of humans by machines is a moral line that we must not cross.” The joint UN–ICRC statement sets out their warning.
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What safeguards can—and cannot—show
Policy safeguards can help manage risks, but a stated expectation is not proof that a particular system satisfies it or that risk has been eliminated. Useful safeguards address the whole chain: how a system is designed and tested, what its users understand, who can authorize its use, how its behavior is monitored, and whether it can be stopped.
- Human judgment: Commanders and operators should retain appropriate human judgment over the use of force.
- Realistic testing and assurance: Testing should assess reliable performance in conditions relevant to intended use, including the possibility of data problems and adversarial manipulation.
- Training and oversight: Personnel should understand system capabilities and limitations, and oversight should address the risk of over-reliance on automated recommendations.
- Auditability: Methods, data sources, and design procedures should be sufficiently transparent and auditable to support scrutiny.
- Ability to intervene: Safeguards should include ways to disengage or deactivate systems that exhibit unintended behavior.
On January 25, 2023, the U.S. Department of Defense announced an update to Directive 3000.09. The department said autonomous and semi-autonomous weapon systems should allow commanders and operators appropriate human judgment over the use of force. It also said personnel authorizing, directing, or operating such systems must use appropriate care and comply with applicable law, treaties, safety rules, and rules of engagement. These are U.S. policy requirements; they should not be presented as evidence that all military AI systems meet them. Details appear in the Defense Department announcement.
The same distinction applies to international endorsement. The U.S. Department of Defense reported that 47 states had endorsed the Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy as of November 22, 2023. That figure is an endorsement count for that date—not a compliance rate, a measure of system safety, or proof that the declaration’s risks have been solved.
How to assess a claim that an AI targeting system is safe
A useful assessment asks what the system does and what evidence supports the claimed safeguards. A broad label such as “AI-assisted” is not enough to determine how much risk it introduces.
- Identify the function. Does the system analyze information, advise a person, or perform critical functions in selecting and attacking targets? Ask where its output enters the chain from information gathering to use of force.
- Examine the human decision. Who is responsible for the decision, what can that person see and understand, and do they have time and authority to reject the recommendation?
- Ask how legal judgments are made. How are distinction, proportionality, and feasible precautions assessed in the specific circumstances of an attack? A system output is not itself an answer to those questions.
- Check the testing conditions. Has performance been tested realistically, including with unreliable data or deliberate manipulation? A result in ordinary conditions does not establish robustness in a contested environment.
- Look for oversight and audit. Are methods and data sources open to appropriate scrutiny? Is there training to address system limits and automation bias?
- Find out what happens when behavior goes wrong. Can responsible personnel detect unintended behavior and disengage or deactivate the system?
These questions help distinguish a concrete safeguard from a general assurance. The sources cited here do not support ranking named deployed targeting systems against one another, so a claim that one such system is safer than another would require evidence beyond the policies and general risk mechanisms described above.
What the available figures do—and do not—tell us
The clearest numerical figure in the cited policy material is the U.S. Department of Defense’s report that 47 states had endorsed the responsible-use declaration by November 22, 2023. It shows the number of endorsing states reported by the department on that date. It does not say how many systems have been tested, how often safeguards are followed, or how frequently AI-assisted targeting produces errors.
No comparable, verified real-world error-rate statistic for AI-assisted military targeting is established in the cited sources. That absence is important when interpreting claims about accuracy: without a defined system, conditions, time period, and measurement method, a percentage cannot reliably describe operational risk.
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