Explainable Decision Recommendations: A Basis for Human Review

Explainable decision recommendations disclose their basis

The National Institute of Standards and Technology distinguishes four fundamental requirements for explainable systems: they should provide evidence or reasons for results, present these meaningfully to the respective users, accurately reflect how the system actually works, and recognize their knowledge limits.^[1] A clear guideline for operational decision recommendations follows: an output is not explainable merely because some explanation appears alongside it.

Explainability also depends on role and situation. A specialist needs different details from an authorized approver, quality assurance, or a later deviation investigation. The same recommendation can therefore have several explanation layers without its substantive basis changing.

Example: Recommendation for an additional in-process check

A simplified, illustrative scenario: during a batch, a system detects that three consecutive measurements are within the stated specification range but are moving toward an alert limit faster than in comparable approved process histories. It recommends taking an additional sample before the next processing step. In this scenario, the alert limit serves as a precautionary notification threshold; it must be distinguished from the specification limit. Trend calculation, selection of comparison histories, and the trigger threshold would need to be defined and tested for actual use.

The first explanation layer states the action and main reason: “Additional sample recommended before step 7: the last three measurements are approaching the alert limit faster than in the comparison histories used. The individual values are still within the stated specification range; an environmental value is missing for part of the comparison period.” This keeps it clear that the suggestion is based on the described trend, not on exceeding the specification range. It does not amount to a complete conformity assessment.

A more detailed view shows the three measurements, equipment and times, the applicable test procedure, alert limit used, and comparison group consulted. It makes the features used to select the histories traceable and identifies which statement remains limited by the missing environmental value. Continuing as planned is shown as an alternative; the observed trend argues against it, not an already established quality defect.

The responsible person reviews the basis. They can order the additional sample, reject the recommendation with a documented reason, or escalate because of insufficient information. The explanation makes the decision-making space visible but does not make the decision.

Five review questions for explaining a recommendation

The following five questions serve as a working aid here. They are not a universal minimum standard; their implementation depends on the method and decision situation. First: What is recommended? The proposed action must be clearly named and distinguished from mere information or warnings. Second: What is the recommendation based on? Relevant measurements, events, documents, rules, and confirmed relationships from experience must remain findable.

Third: Which factors were decisive? A complete data list is of little help if the responsible person cannot identify which deviation or condition substantially influenced the suggestion. Fourth: Which uncertainty or limit exists? Missing information, an unusual case, or a narrow scope must be visible. Fifth: Were alternatives compared, and if so, how? Such a comparison can make the suggestion more understandable. If the method did not assess alternatives, the explanation must not retrospectively claim that it did.

These components do not always have to appear simultaneously on the first screen. A short presentation can show the recommendation, its main reason, and a significant limit. Deeper layers can make data provenance, rule version, comparison cases, and further alternatives accessible. What matters is that shortening the explanation does not hide a substantively important limitation.

Relevant factors are more helpful than a complete wall of data

A complex operation may contain hundreds of data points. Displaying all of them at once creates formal completeness but not necessarily understanding. Explanations therefore select the factors that made the greatest difference to the specific recommendation. However, this selection itself must be traceable and must not be misleading.

Drawing on social science research, Tim Miller shows that people often understand explanations contrastively: they ask not only why an option was recommended, but why this option was suggested rather than another.^[3] An operational explanation can therefore be particularly useful when it identifies the decisive difference—for example, why an additional check is recommended even though the measurement is still within a general tolerance.

The most important factor is not automatically a cause. Statistical relationships, rule conditions, and causal statements must remain distinct in wording. “Occurred together in similar cases” is different from “causes.” An explainable recommendation must not blur this boundary through excessively certain wording.

Relate the explanation to the version actually used

The trend in the example is a derivation. The individual values alone therefore do not explain why it was treated as unusual. The calculation, selected comparison histories, and threshold used are also relevant. If any of these foundations is missing, that particular review remains open.

Data provenance must make it possible to trace which data versions and processing steps produced the recommendation. Measurements corrected later or new rule versions must not appear as the basis of the recommendation at the time.

A recalculation today may produce a different result. It must then be identifiable as a new assessment with its own basis so that both results can be compared meaningfully.

Uncertainty and knowledge limits belong in the explanation

A recommendation may be based on incomplete, late, or only partially comparable information. Such limits are not a technical detail that belongs exclusively in a log. They change the weight a responsible person may assign to the suggestion.

Uncertainty can have different causes: a required context value is missing, a measuring device reports limited quality, a comparison case is only partly similar, or a model has not yet been tested for this special case. These causes should be named separately. A single percentage can easily create an impression of precise certainty even when several different uncertainties coincide.

A threshold must also remain understandable. If a recommendation is triggered at a particular value, it should be clear whether that limit comes from an approved rule, a statistical distribution, or an operational convention. Displaying the threshold alone does not explain why it applies to the current process.

Alternatives and constraints make the decision-making space visible

A recommendation can quickly appear to be the only permissible action if other possibilities remain invisible. A good explanation therefore shows which realistic alternatives existed and which constraint argued against them. This need not be a complete simulation of every conceivable path. What matters is the options actually available under the applicable process, quality, and safety conditions.

For a suggested follow-up check, the alternative might be to continue the process without an additional measurement. If both options were actually compared, the explanation shows which specific unusual observation favored the additional testing effort in that assessment. If, instead, only a rule triggered the suggestion, it names that rule without claiming to have assessed the other option. If an approved SOP excludes an alternative, that rule basis must also be visible.

At the same time, a system must not create a false choice. If a regulatory or safety condition leaves only one permissible path, this is not an open decision recommendation. The interface should then display a binding requirement with its basis rather than pretending that a person can deselect it at will.

Explanations must fit the role and task

Explainability is not a fixed property of an individual text. An explanation is useful only if its intended recipient can understand and review it within their task. Doshi-Velez and Kim emphasize that interpretability must not merely be claimed, but assessed in relation to purpose, user, and task.^[4]

For example, a specialist performing the work needs the affected process step, immediate reason, and permissible actions. Quality assurance additionally needs data provenance, rule version, and comparable cases. For a later investigation, the complete recommendation, its presentation at the time, the human response, and subsequent process history are important.

More detail is not automatically better. Too much technical information can obscure the decisive limitation. Conversely, a simplified explanation must not give the impression that it represents the entire internal calculation. Zachary Lipton warns against equating different meanings of interpretability and post-hoc explanation.^[5]

A recommendation supports the decision; it does not replace it

Decision support systems form the broader system category: they provide information, assessments, or action options for a decision situation. Explainable decision recommendations concern a narrower aspect. They answer why a particular option is suggested in this specific situation and how a person can review its basis.

The recommendation remains a contribution to the decision. Its wording must not make it appear to be a mandatory instruction when the responsible role must choose between several permissible options.

With Human in the Loop, an explainable recommendation can provide a traceable basis for human review or a decision. It neither establishes organizational responsibility nor guarantees that the person can intervene effectively under actual working conditions.

The European guidelines for trustworthy AI connect transparency with human agency and oversight. Explanations should be adapted to the stakeholders concerned and make a system's capabilities and limitations identifiable.^[2] These principles are also useful when an operational recommendation comes not from an AI model but from rules, comparison cases, or a combination of methods.

A rejection may concern the recommendation or its explanation

If a suggestion is rejected, the reason may lie in the comparison method or merely in the failure to present the decisive data gap understandably. These forms of feedback require different investigations. Agreement alone likewise confirms neither content nor explanation accuracy.

A human feedback loop returns such feedback to review. For the explanation, it should first be established whether the objection concerns the data basis, substantive conclusion, or presentation.

What an understandable explanation does not prove

An understandable explanation does not prove that the recommendation is correct, complete, fair, or safe. It may faithfully reproduce faulty inputs, correctly describe an unsuitable rule, or provide a post-hoc justification that inadequately represents actual system behavior. This is precisely why NIST distinguishes a meaningful explanation from its accuracy and from the ability to recognize knowledge limits.

Traceability is not automatically causality either. If similar process histories frequently occurred together with a later quality problem, this relationship may justify an additional check. Without further investigation, however, it does not support the statement that the history caused the problem.

Finally, explainability must not lead to shifting responsibility. A person can conduct a responsible review only if they have sufficient time, authority, expertise, and a real ability to deviate. A formally available explanation button does not compensate for a decision process that effectively prevents human control.

The short explanation must preserve the decisive comparison

In the example, “The values are rising” is not a sufficient explanation. What matters is the described development relative to the selected comparison histories. The relevant data gap must also remain visible when the explanation is shortened.

An explanation is open to review when its statements correspond to the actual basis of the recommendation. Understandable language helps with this review; whether the recommendation and underlying method are substantively suitable remains an additional assessment.