The subject “AI Explainability” must lead to proof, not just to deployment: the expected effect must be measurable and reversible.
Frame the “public of the explanation” point, check the “available factors” point, then decide with an explicit benchmark measure.
1. Key figures
| Number | What it establishes | Source, date and scope | Reading for you |
|---|---|---|---|
| 4 functions | The NIST AI RMF organizes AI risk management around Govern, Map, Measure and Manage. | NIST—AI Risk Management Framework, updated to 2026, AI Systems and Services | An assessment must cover deployment conditions, monitoring and documentation |
| 4 functions | The NIST AI RMF organizes risk management around Govern, Map, Measure and Manage. | NIST AI Resource Center — AI RMF, accessed on July 11 2026, AI systems across sectors | A register or assessment is only valuable if it triggers management decisions |
| item 4 | The AI Act requires suppliers and deployers to aim for a sufficient level of mastery of AI adapted to the people and context of use. | European Commission — AI Act article 4, official text, consulted on July 11 2026, personnel and service providers using AI systems in the European Union | Training must be proportionate to the tasks, decisions and people assigned |
| 2 injection channels | OWASP distinguishes between direct injection in the prompt and indirect injection carried by a file, a page or another external source. | OWASP GenAI — Prompt Injection, edition 2025, consulted on 11 July 2026, LLM applications and connected agents | The model should be treated as an unreliable interpreter and its powers limited in code |
| 5 human capabilities | Article 14 provides that human supervision makes it possible in particular to understand, monitor, interpret, ignore or reverse, and interrupt the system. | European Commission — AI Act article 14, official text, accessed on July 11 2026, high-risk AI systems | A human in the loop is only useful if he has information, skills, authority and a real means of stopping |
These benchmarks limit the decision to an explanation adapted to the decision; they don't take it for you. A published value describes a precise perimeter, a date and sometimes a population different from yours. Read it as a constraint to be tested, not as the promise of an automatic effect. The measurement precedes arbitrage.
On the business side, the comparison maintains a previous state: for this subject, the first source leads to the following operational reading: “An evaluation must cover deployment conditions, monitoring and documentation. » The second reference in the table must also be compared to your perimeter and a local measurement. This distinction between external reference and local measurement protects the analysis against easy extrapolations.
2. Read the sources without overinterpretation
In degraded mode, the budgetary limit is noted: a source is useful when a reader simultaneously understands what it states, the scope it covers and the limit of extrapolation. The five benchmarks below are therefore reread as decision markers, never as causal promises.
For the scope “an explanation adapted to the decision”, external data can only be used to decide if its scope, date, unit and limit are explained. The review should separate what the source establishes, what the team infers, and what a local test still needs to demonstrate.
Concretely, the proof sheet preserves the organism, the title, the URL, the date of consultation, the population, the unit, the method and the reservation of interpretation. It then indicates the decision that the benchmark informs and the local observation capable of contradicting this benchmark. In this file, attach this register to “public explanation” and entrust its review to “Métiers”. Data without a documentary owner ages silently; data with a revision condition remains controllable and can be cited without losing its context.
2.1. Benchmark 1
The NIST Reference — AI Risk Management Framework publishes “4 functions”. Before making a decision, check the date, the population covered and the possibility of replicating the measure locally. The roles are distinct.
2.2. Bench 2
The source NIST AI Resource Center — AI RMF locates the terminal “4 functions” in the field “AI systems across sectors”. It provides an external reference to the diagnosis; it does not replace either a local reference measurement or the analysis of exceptions. These mistakes are costly.
2.3. Bench 3
The milestone “article 4”, published by European Commission — AI Act article 4, falls under the scope “personnel and providers using AI systems in the European Union”. It helps to formulate a testable hypothesis, without transforming an external value into an automatic objective. Control remains human.
2.4. Benchmark 4
OWASP GenAI — Prompt Injection documents “2 injection pathways”. The exact range is shown in the previous table; keep it when comparing this data to your own operations, populations and periods. Nuance matters here.
2.5. Bench 5
European Commission — AI Act article 14 here provides the indication “5 human capabilities”. This information informs a choice; it does not, by itself, demonstrate that the same effect will appear in your context. Each step leaves a trace.
3. Reusable citation sheet
At the time of arbitrage, the calculation unit does not change: a robust citation must be able to be repeated without losing its author, its date, its scope or its limit. The sheet below isolates these elements and links them to a specific decision; it prevents a correct figure from becoming misleading after extraction from its context.
| Field | Content to keep |
|---|---|
| Verifiable assertion | The NIST AI RMF organizes AI risk management around Govern, Map, Measure and Manage. |
| Attribution | NIST — AI Risk Management Framework, updated to 2026 |
| Declared scope | AI systems and services |
| Value or bound | 4 functions |
| Operational reading | An assessment must cover deployment conditions, monitoring and documentation. |
| Decision concerned | Linking “public of explanation” to a local observation before arbitrage |
| Magazine owner | Trades — Avoid digitizing unquestioned friction |
| Condition of revision | Reexamine the citation if the source, scope or “remedy” changes |
4. Introduction: framework the primary risk
The first symptom is not the absence of a tool, but the absence of a link between the points “public of the explanation”, “available factors” and the decision indicator. The “uncertainty” and “recourse” checks then arrive too late to correct the decision.
The concrete risk takes the following form: a fluid justification generated after the fact by the same model. This problem cannot be corrected either by an activated option or by an additional dashboard; it requires a perimeter, a person responsible and contradictory proof.
In current operation, the signal is broken down by segment: our position is therefore clear: the device only has value if the announced effect is observable. The comparison must relate to the situation before the change, then to the same segments after the test. The discrepancy deserves an explanation.
5. Actors and responsibilities
| Actor | Responsibility in the decision | Point of vigilance |
|---|---|---|
| Professions | Describe the actual work, exceptions, and value | Avoid Scanning Unquestioned Friction |
| AI and data team | Designs data, evaluations, models and observability | Measure the complete task and failure cases |
| DSI and security | Manages identities, tools, risks and continuity | Limit scope, secrets and irreversible actions |
| Template Providers | Provide capabilities, limits and developments | Monitor costs, versions, retention and dependencies |
This distribution avoids confusing execution and responsibility. The first operational responsibility falls to the “Professionals” function; the “AI and data team” function provides separate control. The decision is only defensible if each actor knows what it measures, what it authorizes and what it takes back when the accepted limit is crossed. Deferred cost exists.
6. Definition: explanation adapted to the decision
In this guide, the scope “an explanation adapted to the decision” combines the points “public of the explanation”, “available factors”, “uncertainty” and “recourse”. The objective is to obtain people capable of understanding, contesting and taking back a result; the decision is based on the rate of decisions understood and correctly challenged.
When the pilot is launched, the external dependence is documented: the definition is therefore operational: it names the components, the desired effect, the indicator and the limit. A reader can quote it without having to reconstruct the meaning from the rest of the page. This border matters.
7. Why the subject becomes structuring
From the first test, the hypotheses remain rereadable: the sources converge on three terminals: 4 functions, 4 functions and article 4. They do not describe a universal average; they specify thresholds, obligations or operating conditions. In this case, the third source leads to the following operational reading: “Training must be proportionate to the tasks, decisions and people assigned. »
This reading transforms the figures into decision questions: what perimeter do they cover, what uncertainty remains and who can act when the measurement goes beyond the accepted threshold? On an explanation adapted to the decision, this responsibility conditions the desired effect. The calendar serves as proof.
8. Compare four levels of engagement
| Level | What it optimizes | Decision criterion | Limit to make visible |
|---|---|---|---|
| Observation without reference measurement | Apparent speed | public explanation | The result cannot be attributed |
| Narrow-minded pilot | Learning on a flow | Deviation from reference measurement | The tested case may remain too simple |
| Governed deployment | Demonstrated effect on the useful perimeter | “Uncertainty” and “recourse” controls | The recurring cost must remain explicit |
| Reduction or cessation | Control of the main risk | Documented exit threshold | Preserve data, evidence and reversibility |
Concerning an explanation adapted to the decision, the comparison does not point to a universal winner. It makes visible the cost of an absent proof, an overly simple driver or a premature extension. The right level depends on the criticality of the flow, the quality of “available factors” and the concrete possibility of resuming “recourse”. The outing is prepared early.
9. Recommended methodology: seven verifiable steps
Applied to an explanation adapted to the decision, the following method is part of good public and operational practice. It is not presented as a proprietary method of Logiks: its value comes from the order of controls and the possibility, for a third party, to verify each deliverable.
9.1. Formulating the decision
This step transforms intention into control: describing the expected result and relating it to the “audience of the explanation”. Measure what actually changes in the truly open decision and the value that justifies it, including human rework. Document everything in a note cadrage which names the decision, the limit and the person responsible.
9.2. Measuring the starting point
Under real constraints, the stopping decision remains possible: to move forward without hiding the deferred cost, you must observe the decision indicator before any modification. Compare before and after on the initial situation and its variations between segments, then have an initial measurement dated and broken down by useful segment reread by an actor who did not design the test.
9.3. Trace Critical Path
Expected action: Link “available factors” to relevant data, teams and dependencies. Start on a perimeter where the team can still get back. The expected proof concerns the exceptions encountered by the teams operating the system; record it in a map of exceptions, dependencies and owners.
9.4. Laying down safeguards
The work consists first of all in framing “uncertainty” with limits, rights and a recovery procedure. Do not retain an ideal demonstration or an overall average: observe the limits, the rights of action and the possibility of going back. The useful deliverable is a control matrix that makes cost and reversibility visible.
9.5. Test the difficult case
At this stage, “recourse” must be tested in a representative scenario, then in a degraded scenario. Involve the person who handles the exceptions, then compare the result to the nominal behavior, the failure caused and the quality of the recovery. You must be able to provide a report of the nominal scenario, the failure and the human recovery to a decision-maker absent from the project.
9.6. Build evidence
At each check, the changes are versioned: here, the action consists of comparing result, errors, interventions and complete cost at the starting point. Run the check on a normal case and a degraded case, keeping the gap between the initial promise and the recorded facts as a criterion. The concrete output takes the form of a file of logs, deviations and decisions readable by a third party.
9.7. Decide and Review
During cadrage, the fallback procedure is accessible: this step transforms the intention into control: assign the review and follow the measurement according to an explicit cadence. Measure what actually changes in the threshold that triggers a correction, extension or shutdown, including human rework. Document everything in a review rule with correction and stopping thresholds.
10. Logik tips: proof, mastery and reversibility
Our priority is the following risk: a fluid justification generated after the fact by the same model. Start where this fragility already produces an expectation, a loss, or a contested decision; the prestigious perimeter can wait.
When an arbitrage is contested, action rights are documented: keep the baseline metric at the level where a team can act. A quarterly average does not replace an observation by course, by cohort or by type of exception; the marker must remain actionable.
Treat “public explanation” as a documented decision. A manager, a hypothesis, a limit and a review date are better than an adjustment whose origin no one knows.
Experience “uncertainty” with “recourse” and then with a degraded recovery. The test should reveal operation and operating cost, not just confirm that the demonstration holds up.
Only extend the system if the observed facts support the desired effect and if “available factors” remain controllable by a person outside the project.
In this file, the recommendations express a sequence judgment: make the risk observable, test the hypothesis relating to “uncertainty”, then commit the resources. Sophistication comes after the demonstration of the announced effect; it does not replace it. This evidence is local.
11. Decision grid
| State | Signal observed | Expected proof | Cautious decision |
|---|---|---|---|
| To frame | “public explanation” exists without a named result | dated reference measurement | Do not engage the entire perimeter |
| As a pilot | “available factors” is tested on a real flow | Deviation from starting point | Include a representative exception |
| Governed | “uncertainty” has a manager and a review | Stability, cost and incidents | Document degraded mode |
| To expand or stop | “recourse” allows a decision | Net worth and residual risk | Apply exit rule |
The grid does not automatically produce arbitrage on an explanation suitable for the decision. On the other hand, it forces the teams to show their hypotheses about the “public of the explanation”, their thresholds and their responsibilities; a disagreement is then explicit and can be resolved. Reversibility decides.
12. Frequent errors
12.1. Consolidate activation and result
Enabling “public explanation” does not prove that the expected effect is achieved. This error shifts the debate towards the tool while the decision concerns an observable change.
12.2. Optimize the first available indicator
For the responsible team, the full cost becomes apparent: a convenient proxy may advance while the decisive metric deteriorates. Link each signal to a decision and a guardrail.
12.3. Ignore exceptions
In the event of an exception, the measurement date is recorded: the nominal path often hides the fragility described above. Test a borderline case, a failure and how the team regains control.
12.4. Leave an addiction without an owner
When “available factors” is everyone’s responsibility, no one decides the incident or the cost. Assign the decision before deployment.
12.5. Present risk as a formality
Documenting “uncertainty” without correcting the system produces facade conformity. The record must show a check performed and its result.
12.6. Extend without exit rule
If “recourse” does not allow a decision to be made, the pilot continues through inertia. Set continuation, correction and termination thresholds in advance.
13. Action Plan 30 / 60 / 90 days
13.1. Days 1 to 30: establishing the starting point
- describe the decision, the scope and the person responsible for it;
- record the initial value of the indicator before any modification;
- inventory dependencies and their exceptions;
- write the main risk and its detection condition.
Before any extension, operations can resume: the first phase serves to make the disagreement visible. At thirty days, management must know the baseline measurement, the missing data and the specific case on which progress will be judged.
13.2. Days 31 to 60: testing the critical path
- implement primary control over a representative flow;
- test the recovery in a normal then degraded situation;
- record errors, human interventions, delays and costs;
- compare the observations to the initial scenario.
After release into production, exceptions are logged: this pilot does not only seek to demonstrate that the technology works. It must establish whether the system advances the selected indicator without shifting a disproportionate burden towards the operation, users or a supplier.
13.3. Days 61 to 90: decide and organize the continuation
- consolidate the evidence and have its limitations reread;
- assign each recurring control to a named function;
- confirm the next review date and discharge procedure;
- extend only if the facts support the effect initially announced.
If the measurement diverges, the next deadline is planned: at ninety days, the initial hypothesis must be demonstrated or refuted. Three decisions remain legitimate: extend, correct or stop the perimeter; continuing without a threshold does not constitute a fourth option.
14. FAQ
14.1. How to define an explanation adapted to the decision?
It is a decision framework applied to an explanation tailored to the decision. The approach links “public of explanation” to “uncertainty” and “recourse” controls, with a reference measure, those responsible and an exit rule.
14.2. What to start with?
In the presence of a third party, local verification can be replicated: start with an actual decision, a baseline measurement, and a previously observed manifestation of the primary risk. The tool comes after this cadrage.
14.3. What budget should be retained?
Because the context evolves, the stopping rule is known: add preparation, integration, operation, control, training, incidents and exit. Compare this full cost to the expected value, not just the license or campaign price.
14.4. How long should the test last?
The test must cover a complete measurement cycle and at least one exception related to “uncertainty”. Its duration derives from this observation, not from an arbitrary standard.
14.5. When to scale?
Scale up when progress remains stable, the “pathway” is controlled and responsibilities, costs and exit conditions are documented.
15. Conclusion
During the review, the hypothesis can be contradicted: the decision is solid when a common measure links the technical, business and financial choices. The number of options activated is less important than the ability to explain discrepancies, deal with exceptions and reverse a choice that has become costly.
The pivot is simple: the project “an explanation adapted to the decision” must no longer be a project to be delivered, but a capacity to govern to produce the announced effect. The test must stand.
16. Main sources
- NIST—AI Risk Management Framework — updated to 2026 — AI systems and services.
- NIST AI Resource Center — AI RMF — accessed 11 July 2026 — AI systems across industries.
- European Commission — AI Act article 4 — official text, consulted on July 11 2026 — personnel and service providers using AI systems in the European Union.
- OWASP GenAI — Prompt Injection — edition 2025, consulted on 11 July 2026 — LLM applications and connected agents.
- European Commission — AI Act article 14 — official text, accessed July 11 2026 — high-risk AI systems.