Standardizing Review with AI ── A Shared Language That Reduces Reliance on Individuals── A yardstick that brings everyone closer to the same judgment

01The Problem of Individual Reliance ── The Same Material, a Split Verdict

First, let us break down why verdicts split. Reliance on the individual does not happen "because the reviewer is careless." Even with a diligent review, the result changes from person to person for the reasons below. In fact, telling people to "be more careful" without understanding the source will not reduce the variation.

Read the monitoring project reports on sales information provision activities that the Ministry of Health, Labour and Welfare publishes, and you find that most deviations arise not from flashy sales pitches but from "the single sentence that slipped past because no one caught it." Blatant violations anyone can stop. What is dangerous is the borderline expression that is visible to some reviewers and not to others. Reducing individual reliance means making this "visible-or-not" visible to everyone in the same way. The destination of standardization lies here.

02Putting the Standard into Words ── Turning "Somehow" into a Yardstick

The first step of standardization is neither AI nor a mechanism. It is putting the judgment inside a veteran's head into words. "This expression somehow feels risky" ── this hunch is genuine knowledge, years of cases soaked into the body. But because it is not in words, no one but the person can use it. So we first take it out.

Merely listing dangerous words is not enough to take it out. What matters is writing the conditions. Take the word "effective." Ban it across the board and you also reject sentences that use it correctly within the approved scope. Instead, put into words "when it is a problem and when it is not" ── permitted if backed by the approved efficacy and effect, not permitted if it hints at superiority beyond that. This written-out set of "conditions for drawing the line" is called a scoring rubric (= gloss: a table listing what to look at and how).

The very act of putting it into words has a large effect. Even before introducing AI, when reviewers discuss "what to do in this case" and write the conditions side by side, the range of interpretation narrows. A standard that cannot be written cannot be shared. Conversely, a standard that can be written works the same way for newcomers and veterans alike. Standardization begins, before any tool is introduced, with aligning the words first.

03Holding Consistency with AI ── Applying the Same Yardstick, Untired, Unwavering

Once the standard is in words, AI first takes effect there. AI's greatest strength is not cleverness but consistency. A human tires by the thousandth item; AI applies exactly the same yardstick to the first item and the thousandth. Neither that day's deadline nor the flow of materials seen just before shakes AI's judgment. The wavering from "condition on the day" and "order of viewing" raised in Section 1, AI in principle does not have.

Replace the yardstick that differed by reviewer with a single shared yardstick called AI. This is the substance of using AI for standardization. Even when the person changes, even when the day changes, the same material gets the same finding back. This reproducibility shaves away the trickiest part of individual reliance.

One thing must not be misunderstood here. AI's consistency is not a guarantee of correctness. AI merely applies the given standard the same way every time. If the standard itself is wrong, AI repeats the mistake consistently. Being uniformly wrong is, if anything, harder to notice than being scattered-wrong. So before making it the shared yardstick, a human must confirm that the yardstick is graduated correctly. What AI aligns is "the same judgment," not "the correct judgment."

04Operating the Checklist ── Making the Aligned Standard Run on the Floor

When the worded standard is dropped into a form usable in daily review, in most cases it becomes a checklist. "Is it backed by the approved efficacy and effect?" "Are there superlative or absolute expressions?" "Are there words that suggest a transaction?" ── the conditions written out in Section 2, each turned into a confirmation item. AI fills this list automatically for each material and shows the reviewer the points that were flagged.

There are two knacks of operation here. One is that the checklist is not a tool for filling in but a frame for preventing misses. When ticking the items becomes the goal itself, review turns into mere form. A pilot reads out the confirmation list before takeoff not to fill in the list but to prevent misses that come from habit. The material-review checklist is the same: the goal is not "the ticks" but "zero misses."

The other is that the checklist goes stale. As approval information changes and new patterns of violation are found, you add and remove items. Who updated it when, and which version was used for the review ── recording this is continuous with last time's guardrails. Put the standard into words, drop it into a checklist, stamp the version. Standardization is not made once and done; it is something kept by running it continuously.

05Ripple Into Education ── The Standard Becomes Teaching Material for Newcomers

Standardization has an effect beyond aligning review. A worded standard becomes teaching material for newcomers as it is. Newcomers once stood beside a veteran and, at each finding, stole and memorized "why did they stop this now." It took time, and what was conveyed differed by teacher. When the standard is in words, a newcomer can first learn the yardstick itself.

Put AI in between and this learning speeds up. A newcomer reviews a material, and AI returns findings from another perspective. Set the two side by side, and the newcomer learns on the spot "the perspective I missed." Even without a veteran sitting beside them item by item, AI keeps returning findings along the standard, so the number of practice reps builds up. Individual reliance tends to be handed down across generations, but with an articulated standard and AI findings, that chain can be cut.

06The Limit ── Standardization Cannot Erase the Delicate Judgment

We have described the effects of standardization so far; let us honestly set down the limit. What can be fully written into a standard is only the part where the line is clear. The difficulty of material review lies mostly on the border ── just inside the approved scope, or one step outside. This delicate judgment is settled by context, hard to put into words, and exactly where AI is weakest. Standardization does not erase this border.

If anything, aligning too much brings another harm. Apply a standard swung to the safe side mechanically, and expressions used correctly within the approved scope get rejected in bulk. The floor gets buried in the work of pushing back "this is a false positive," and eventually stops believing the standard itself. Conversely, once reviewers start thinking "I only need to look at what is in the rubric," no one stops a new kind of deviation that is not in the table. Standardization is not a tool for making thinking unnecessary.

07Connections to Other Chapters ── The Shared Language Ties the Whole Series Together

The standardization assembled in this volume connects to the other volumes of this series, and to the sister series, as follows. A shared language is not a standalone technique but the ground that lets everyone use the tools of each volume.

• AI Material Review Vol. 4 ── Giving Rules to AI ── This volume lifts the guardrails assembled last time, from a yardstick that differed by reviewer to a shared language held by everyone. Hold the fence together, and individual reliance drops.
• AI Material Review Vol. 3 ── Efficacy and Effect Within the Approved Scope ── The center of the standard to be standardized is drawing the line of the approved scope. Drop the thinking confirmed in Vol. 3 into words that everyone can use the same way.
• AI Material Review Vol. 6 ── The Real Power and Limits of AI Review-Support Tools ── How far the tool that actually runs the standard aligned in this volume can go, and where it should return to humans. Examined concretely next time.
• AI Programming Vol. 1 ── The Basics of Code Generation ── The idea of applying the same standard the same way every time to hold consistency shares the same skeleton as verifying AI-written code uniformly with automated tests.

Individual reliance is not the reviewer's negligence. Difference in experience, range in interpreting the standard, condition on the day, order of viewing ── even with a diligent review, judgments vary. Standardization means, to reduce this variation, first putting a veteran's "somehow" into words, turning it into a conditional yardstick, making that a single shared yardstick called AI, and sharing it among everyone. AI's strength is not cleverness but a consistency that neither tires nor wavers. Yet that consistency is not a guarantee of correctness, so whether the yardstick is graduated correctly is something a human confirms first.

The aligned standard runs as a checklist and also becomes teaching material to raise newcomers. But what must not be forgotten: what standardization can erase is only "the clear line," while the delicate judgment at the border remains. Align too much and you reject even correct expressions, and reviewers stop thinking. The aim of standardization is not to replace human judgment but to concentrate strength where humans truly need to use their heads. Next time, we examine head-on how far an AI review-support tool that actually runs this standard can go, and where it runs out of strength.