Set up human review as a decision process—not a final approval click. Define the decision and its risks, assign reviewers with relevant expertise and real authority, give them enough context to assess each case independently, and make it practical to challenge, escalate, or stop the AI workflow. Keep a usable decision record and reassess the process as the system and its effects change.
1. Define the decision and the AI’s role
Start by describing the decision in plain language. Record who may be affected, what harms a wrong or delayed decision could cause, and whether the outcome can be reversed. Specify whether the AI recommends, ranks, flags, or makes a decision. These details determine how much review the process needs; the same approval pattern will not suit every use.
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Identify the applicable law and regulatory requirements for the use case and jurisdiction before choosing the oversight model. For covered high-risk systems, the EU AI Act says oversight measures must be proportionate to “the risks, level of autonomy and context of use” of the system. See Article 14 of Regulation (EU) 2024/1689.
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2. Assign named people and decision authority
Make responsibility explicit rather than assigning oversight to an undifferentiated team. The NIST AI Risk Management Framework’s Appendix C says human roles and responsibilities in AI decision-making and oversight need to be clearly defined and differentiated.
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- Name the operational reviewer and the person who owns the final decision.
- Identify who handles escalation, appeals, incidents, and ongoing monitoring.
- Confirm reviewers have the relevant domain competence and training.
- Give reviewers practical authority to reject or change an AI recommendation—not just formal permission on paper.
Check that the workflow, workload, and management expectations allow reviewers to use that authority. If the process rewards speed or agreement but makes a challenge difficult, its design can undermine meaningful review.
3. Give reviewers enough to assess the case
Show the AI output alongside the information needed to evaluate it: relevant inputs, the system’s intended use and known limits, and case-specific context. Decide what evidence or human factors matter that the system may not have considered. The UK Information Commissioner’s Office (ICO) warns that if reviewers can access only the data used by the AI, they may not be considering additional factors and the review may not be sufficiently meaningful. Its guidance is titled “How do we ensure individual rights in our AI systems?”.
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Do not treat a confidence score or generated explanation as proof that a result is understandable. Define what a reviewer needs to understand to make this decision, then check whether they can identify relevant inputs and recognize when the output may be wrong. The ICO notes that explanation methods can mislead when misused, particularly with high-dimensional models.
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Write down the conditions for disregarding or overriding a recommendation, seeking a second opinion, escalating uncertainty, and safely halting the system or workflow. Specify who receives an escalation, what happens while it is unresolved, and how a decision can be corrected if new information emerges.
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For high-risk systems covered by Article 14 of the EU AI Act, human oversight measures must enable the assigned people to decide not to use the system, disregard, override or reverse its output, and intervene or safely interrupt it. Design the operational steps around the actual system and decision; a nominal override button is not enough if using it cannot change what happens.
5. Reduce automation bias from the start
Reviewers can give an AI recommendation too much weight simply because the system produced it. Training matters, but it should not be the sole safeguard. The ICO advises putting controls against automation bias in place from project scoping and design through development and deployment.
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- Train reviewers on the system’s intended use, limits, and plausible failure modes.
- Structure the review so people examine relevant evidence and additional factors, rather than merely confirming the AI’s answer.
- Build in a way to challenge an output and get help when it appears anomalous or uncertain.
For covered high-risk systems, Article 14 also requires attention to the risk of automation bias, including where AI outputs are used by people making decisions.
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As a practical governance measure, record enough to understand what happened in a particular case: the system and version, decision context, relevant AI output, reviewer, final decision, rationale, and any override, escalation, or later correction. Set retention, access, and privacy rules under the law that applies to the use case. This recordkeeping approach is an implementation measure informed by the Act’s logging, traceability, and oversight duties; it is not a universal retention schedule.
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7. Reassess the oversight workflow
Review whether the system, its use, or the surrounding environment has changed; whether reviewers can still spot anomalies; and whether escalation and override routes work in practice. Look at corrections and outcomes for signs that the process needs adjustment. Article 14 addresses detecting and responding to anomalies, dysfunctions, and unexpected performance. The European Commission’s AI Act overview also describes deployers’ responsibilities for human oversight and monitoring after a system reaches the market.
What the law requires depends on the system and jurisdiction
The EU AI Act’s human-oversight requirements apply to high-risk AI systems within its scope, not to every AI tool or every consequential decision. The European Commission lists categories including employment, education, certain essential services, biometrics, law enforcement, migration, and justice. Its overview, reflecting changes made by the AI Omnibus that entered into force on 27 July 2026, gives 2 December 2027 as the transition date for Annex III rules. Check the current legal text and classification of the particular system before relying on a compliance conclusion.
There is a separate two-person verification rule in Article 14(5) for a limited category of high-risk remote biometric identification systems. It concerns verification by at least two competent, trained, and authorized people, with a stated exception for certain law-enforcement, migration, border-control, or asylum uses where the law considers the requirement disproportionate. It is not a general rule that every high-stakes AI decision needs two reviewers.
The ICO material is UK data-protection guidance, and the ICO says it is under review following changes made by the Data (Use and Access) Act. Use it as guidance on review design, and verify the current UK legal position for the relevant deployment.
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