Preventing an AI system from selecting or acting on the wrong target takes more than improving its accuracy score. Define exactly what it may identify or do, test it in the conditions where it will be used, independently verify consequential identifications, and give an accountable person the information and authority to reject, reverse, or safely stop its output. The controls should become stricter as potential harm rises and actions become harder to undo.
First define what “target” means in this system
The word target covers very different decisions. A military system may identify an object or person and support or initiate engagement. A biometric system may match a person to an image or record. A software agent may choose a webpage, file, account, or button and then take an action. The consequences and failure paths differ, so safeguards must match the actual use rather than assume one set of rules covers all three.
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Start by writing down the system’s permitted purpose and authority. Specify what it is meant to identify or act on, what it must not do, which data and operating contexts are permitted, and who remains accountable for the outcome. Make those boundaries visible to operators and decision-makers. Also ask whether AI is necessary for the task or whether a lower-risk method can meet the need.
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This definition should distinguish an AI recommendation from an action the system can take itself. A model that flags a possible match for review creates a different risk from one whose identification directly triggers an irreversible action. Alexander Blanchard and Laura Bruun’s June 2025 SIPRI comparison describes this distinction in military targeting: autonomous weapon systems can have a direct path from target identification to engagement, while AI decision-support systems can contribute to harm when a person acts on a false identification.
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Test the mistakes that matter, not just overall accuracy
A single accuracy figure can hide the errors that matter most. A false positive means the system selects something that is not the intended target; a false negative means it misses the intended target. Which error is more dangerous depends on the use. Evaluation should therefore ask what happens after each type of error, who might be affected, and whether the resulting decision can be reversed.
Evaluate for the intended use and operating conditions
Test the system for the specific task, data, environment, and affected populations in which it will operate. Include representative cases as well as difficult or unusual cases and foreseeable misuse. Check whether the data or operating environment has limitations that could make an apparently confident output unreliable. The Intelligence Community’s June 2020 Artificial Intelligence Ethics Framework recommends considering metrics such as accuracy, precision, and recall and asking how results were independently verified.
Set performance thresholds and operational limits that reflect the consequences of error. The cited frameworks support risk-based evaluation, not one accuracy threshold that is appropriate for every AI system. A threshold should be a decision rule for the defined use, not a substitute for examining failure cases. If the system is moved to a new population, environment, task, or level of authority, assess it again before relying on it there.
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Review results across relevant populations and operating conditions rather than relying only on an aggregate score. Look for cases where errors cluster, where the input differs from what the system was evaluated on, or where the system’s confidence could encourage an operator to overlook contrary evidence. Record what the system cannot reliably establish so users do not treat an identification as proof of something beyond its tested purpose.
Require a separate check when a wrong identification could cause serious harm
For consequential decisions, a second look should be a genuine verification step, not a second person simply endorsing the first person’s conclusion. Define what evidence the verifier must examine, what discrepancies require escalation, and whether the action must wait until the check is complete. The check should be independent enough to catch a mistaken identification rather than reproduce it.
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One specific legal example should not be generalized to every AI target-selection system. Article 14 of the EU AI Act sets a two-person verification rule for certain decisions involving high-risk remote biometric identification, subject to exceptions under applicable law. It is not a universal requirement for all biometric uses, military targeting, or AI systems. The European Commission AI Act Service Desk’s displayed consolidated text is dated 27 July 2026, and the Service Desk says its summaries are not legally binding; the applicable legal text and jurisdiction matter.
Outside that defined rule, whether a separate check is needed should be determined by the risk, context, and applicable law. A useful practical question is whether the proposed action would still be permitted if the identification were wrong. If the answer is no and the harm would be difficult to reverse, require stronger verification or do not let the system trigger the action on its own.
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A human checkpoint is not meaningful merely because a person clicks “approve.” Reviewers need enough time, context, and relevant information to interpret the output and understand its limits. They also need to recognize automation bias: the tendency to give an automated recommendation more weight than independent evidence warrants.
Give reviewers the ability to disagree
Assign a person or role that can decide not to use the system, disregard its output, or reverse a recommendation. Article 14(4)(d) of the EU AI Act describes this kind of authority for applicable high-risk AI systems. The person must be able to act on that authority in practice; an approval process that makes rejection impossible or penalizes it is not effective oversight.
Provide an effective intervention and stop path
Define how an authorized person can intervene while the system is operating and how to bring it to a safe halt. Article 14(4)(e) of the EU AI Act refers to intervention or interruption through a stop button or a similar procedure that allows the system to come to a halt in a safe state, for the high-risk systems to which the article applies. For any system where interruption is a safeguard, specify who can invoke it, how quickly it takes effect, and what safe state means in that use.
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Make responsibility clear at each stage: who sets the system’s permitted role, who reviews an identification, who authorizes an action, and who can restrict or stop use when a problem appears. The UK Ministry of Defence’s policy, Ambitious, safe, responsible, emphasizes that greater autonomy does not necessarily mean greater capability. The practical implication is to judge a system by whether it performs safely in its defined role, not by how much authority it can be given.
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Oversight should be proportionate to the system’s risk, autonomy, and context. ISO/IEC DIS 42105 is draft human-oversight guidance, not a finalized rule; it notes that monitoring and controlling capability should be sufficient, though not necessarily perfect. That does not mean accepting preventable gaps: reviewers still need a workable view of the system and a reliable way to intervene when the stakes demand it.
Protect software agents from misleading inputs
For an agent that reads webpages or other third-party material and operates a computer, the input path itself can misdirect the system. A webpage may contain text that attempts to act like an instruction. OpenAI’s Operator System Card identifies prompt injection from third-party websites as a risk and describes confirmation and human oversight for certain consequential actions.
Treat external content as potentially misleading rather than as trusted authority. Define which actions require confirmation, especially actions with significant or hard-to-reverse consequences, and monitor the agent’s behavior around those actions. A confirmation prompt should make clear what the agent is about to do and give the user a real opportunity to stop or correct it.
These are vendor-described mitigations for Operator, not independently verified proof that the risk is eliminated or a guarantee that every agent is safe. The general safeguard is to limit what an agent can do without confirmation and to retain effective oversight over consequential actions.
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Keep safeguards active after deployment
Evaluation at launch is not enough. Performance can change when the model, input data, user behavior, or operating environment changes. Establish a documented review interval appropriate to the use, monitor for accuracy problems and distributional change, and define who investigates when results no longer meet requirements. A material change in purpose or context should prompt a fresh assessment before continued use.
Keep records that let an accountable team understand how the system was used and investigate a concern. Depending on the application, document:
- The system’s intended purpose, permitted use, and limits.
- The model version and relevant data and test information.
- How identifications were verified and what decisions followed.
- Who was responsible for review, authorization, intervention, and escalation.
- When performance or operating conditions were reviewed and what corrective action was taken.
Specify in advance who can restrict, remediate, or stop the system. If monitoring identifies changed conditions, unexpected errors, or a failure to meet the required threshold, the responsible team needs an actionable response—not just a record that something went wrong.
Choose controls by the action path and level of risk
Compare control designs using the actual operating context. The following questions help expose where an identification could turn into harm:
- Action path: Does an AI output inform a person’s decision, or can it trigger an action directly?
- Harm and reversibility: How serious would a mistaken action be, and how easily could it be undone?
- Human control: Can an authorized person understand the output, reject or reverse it, intervene, or stop the system in time?
- Verification and records: Is a consequential identification independently checked, and can the performance, limits, version, and resulting decision be reviewed?
- Context: Do evaluation and ongoing review reflect the data, environment, affected populations, and applicable law?
A design with more autonomy needs safeguards suited to its faster or more direct action path; simply adding an approval step does not solve the problem if the reviewer lacks time, information, or authority. Conversely, a recommendation-only system still needs controls if people may act on its output in ways that cause serious harm. Scale verification, oversight, and monitoring to both the potential harm and the system’s autonomy.
Distinguish common safeguards from legal requirements
The relevant sources address different settings: the EU AI Act concerns high-risk AI systems and includes a specific provision for a defined biometric-identification category; SIPRI’s June 2025 report addresses autonomous weapons and AI-enabled decision support in military targeting; and OpenAI’s system card discusses an internet-using agent. They do not establish one law, verification rule, or technical threshold that governs every AI system that selects or engages a target.
For military uses, biometric identification, or software agents, describe the purpose and jurisdiction before drawing a legal conclusion. The controls in this article are a risk-based safeguard framework; they do not replace the laws and rules that apply to a particular deployment.
Frequently Asked Questions
Which performance metrics should be used to test an AI target-selection system?
Choose measures suited to the intended use and the consequences of error. Accuracy, precision, and recall are among the metrics identified in the Intelligence Community’s AI Ethics Framework, but no single score or universal threshold fits every use. Test representative and difficult cases, relevant populations, operating conditions, foreseeable misuse, and how results were independently verified.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsDoes every AI system that identifies a target require two-person verification?
No. The EU AI Act specifies two-person verification for a defined category of high-risk remote biometric identification decisions, subject to exceptions under applicable law. That requirement should not be generalized to every biometric application, military targeting system, or software agent. Check the applicable law and jurisdiction.
Is a human approval step enough to prevent a wrong-target decision?
Not by itself. A reviewer needs sufficient information and time to assess the output, understand its limits, and recognize automation bias. They also need genuine authority and practical ability to reject or reverse the recommendation, intervene, or stop the system safely when appropriate.
The Bottom Line
Reduce wrong-target risk by limiting the system’s role, evaluating the errors that matter in its actual context, independently checking high-consequence identifications, and giving accountable people meaningful authority to disagree or stop use. Keep those safeguards active through monitoring and reassessment. No one threshold, approval step, or legal rule applies to every AI system; match controls to the action path, stakes, autonomy, and jurisdiction.
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