Verification is a planned function in the AI process
Humans ensure context, responsibility, exception handling, and professional justification.
Your task is not to approve every output across the board. A reliable review requires a clear subject of review, sufficient information, decision-making competence, and a defined escalation path.
Human-in-the-loop is therefore not a label. It is a role and process design.
Starting point
By the end of 2023, generative models had been practically tested in many knowledge-based tasks.
A recurring pattern emerged:
- The output was fast.
- The wording was convincing.
- Individual errors were difficult to detect at first glance.
- Company context or exceptions were missing.
- Those responsible had to decide whether the result could be used.
The obvious answer was often: 'A human will check that.'
This wording was too imprecise.
Who checks? What exactly is checked? According to what criteria? With what sources? What happens in case of uncertainty? How much time is available? Is the person checking allowed to stop the process?
Without answers to these questions, human review becomes a formal endpoint. It creates a sense of control without actually ensuring control.
The four functions of human review
1. Assess context
An AI system processes the information available to it. It does not reliably recognize the unspoken business, social, or professional significance a case possesses.
people can assess:
- whether a request is sensitive,
- whether a tone is appropriate,
- whether an exception exists,
- whether additional information is needed,
- whether the proposed action fits the real goal.
2. Ensure technical feasibility
An output can be plausible and yet incorrect or incomplete.
Technical review compares the result with:
- binding sources,
- applicable rules,
- actual performance,
- current state of knowledge,
- permissible scope of assertion.
The reviewing person does not thereby assume the responsibility of the model. They assume responsibility for the use of the result.
3. Handle exceptions
Robust automation knows a standard path and an exception path.
Humans decide where:
- Information is missing,
- sources contradict,
- risks are increased,
- the rule does not apply clearly,
- an irreversible action is imminent,
- affected persons have special protection interests.
A system must be able to detect and visibly hand over such cases.
4. Enable learning
Human corrections are not just individual case work.
They show:
- which errors recur,
- which source is missing,
- which prompt is unclear,
- which rule needs to be supplemented,
- which case should not be automated.
Those who document these feedbacks improve the entire process. Those who merely correct them silently keep the weakness invisible.
Strategic Classification
The NIST AI Risk Management Framework linked trustworthy AI use in 2023 with governance, contextual understanding, measurement, and risk treatment.
ISO/IEC 23894 also classified risk management as an ongoing organizational task. In this context, humans are not merely the last instance of control. They are part of the design, evaluation, and monitoring.
The OECD AI Principles have emphasized human-centered values, transparency, robustness, and accountability since 2019.
For companies, this means:
Human review must be designed proportionally to the potential impact.
The role model of the review
How the control level is determined
Low impact and easy correctability
Example: internal structuring variants.
Possible are:
- visible labeling as a draft,
- random samples,
- simple plausibility check,
- no automatic external use.
Medium impact
Example: prepared customer communication.
Usually required:
- professional review before sending,
- access to binding sources,
- documented correction,
- clear approval role.
High impact or low reversibility
Example: binding decision, system action, or sensitive statement.
General verification routines are not sufficient here. Stricter controls, documented decisions, and, if necessary, specialized legal or technical verification are required.
Perspective from practice
A good testing stage is designed so that the responsible person quickly recognizes:
- which inputs were used,
- which sources were used,
- what uncertainty exists,
- which rule was applied,
- what change the system suggests,
- which consequences the release triggers.
A bad review stage only shows the finished result and requires a click on "Confirm".
Then the human must reconstruct the entire process. Under time pressure, a so-called confirmation bias easily arises. The output is confirmed because the system already appears professional and the review is too complex.
What companies should not do
Companies should not use human-in-the-loop as a blanket safeguard in presentations.
A person in the process does not prevent an error if they:
- has too little context,
- has no time for review,
- may not intervene,
- knows no clear criteria,
- cannot see the source,
- is responsible for too many cases at the same time.
You should also not check every process with the same strictness. Over-checking makes the process uneconomical. Under-checking makes it risky. The level of control must match the impact.
Consequences for companies
Human review remains necessary because AI systems do not assume operational responsibility.
This check can become more efficient, risk-based, and more strongly supported by technical controls with increasing quality. However, it does not disappear where context, exceptions, and justification remain relevant.
The right question is therefore not:
Do we still need a human?
But rather:
What human decision must remain possible at what point with what information?
Subject-matter connection
Design review and escalation paths
AI governance defines which results are automatically processed further, checked randomly, technically reviewed, or explicitly released. SDC Discovery makes these control points and responsibilities visible.
Classify human verification and approval stages
Sources and technical foundations (5)
- National Institute of Standards and Technology, „Artificial Intelligence Risk Management Framework (AI RMF 1.0)“, 2023. Open source
- ISO, „ISO/IEC 23894:2023. Artificial intelligence. Guidance on risk management“, 2023. Open source
- OECD, "OECD AI Principles". Open source
- OpenAI, "GPT-4 System Card", March 2023. Open source
- Information Commissioner’s Office, „Guidance on AI and data protection“, 2023. Open source
