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Explainable AI still struggles to speak human because an explanation can be technically plausible yet fail the person who needs to act on it. A useful explanation must reflect how the system produced its output, make sense to its intended audience, and help with the decision at hand. Those are separate requirements—not outcomes that plain language alone can guarantee.
What does it mean for AI to explain a decision?
People often use “explainability” and “interpretability” as if they mean the same thing. NIST draws a distinction that helps clarify what an explanation needs to do: explainability concerns how a decision was made, while interpretability concerns why an output matters in the context of the system’s intended function. Transparency addresses a more basic question: what happened.
These ideas connect, but none guarantees the others. A system might reveal information about its operation without explaining why a particular recommendation appeared. An explanation might describe how a score was produced without clarifying what that score means for a person’s case. And an account that sounds clear can still misrepresent the system’s actual process.
Why can’t AI explain its decisions in plain language?
One explanation cannot serve every audience
“Human” is not a single audience. A data scientist diagnosing model behavior may need technical detail. A caseworker reviewing a recommendation may need to know which information influenced it and what to verify. A person affected by the decision may need a clear account of what the outcome means for them and what, if anything, they can do next.
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NIST guidance says explanations can be tailored to a user’s role, knowledge, and skill. Its example is instructive: a clinician may need technical reasons for an AI output, while a patient may need personal context. A single explanation designed for everyone risks being too technical for some people and too thin to be useful to others.
Clarity and faithfulness are different tests
A readable explanation is not necessarily a faithful one. A feature attribution, for example, may identify inputs associated with an output, but a person still needs to know what those inputs mean in the decision context. Conversely, a fluent narrative may feel convincing while failing to accurately represent how the system reached its result.
NIST’s proposed principles for explainable AI treat these as distinct responsibilities: provide evidence or reasons for outputs; make explanations meaningful to individual users; correctly reflect the process that generated the output; and operate within designed conditions or with sufficient confidence. These are proposed principles, not a settled universal standard, but they make clear why “put it in plain English” is not a complete solution.
The explanation must answer a useful question
“Why did you do that?” is a natural question to ask an AI system, especially when its output affects a consequential decision. But a useful answer depends on what the person is trying to understand. Are they checking whether the system used relevant information, diagnosing an error, deciding whether to rely on a recommendation, or trying to understand an outcome that affects them? Without that context, even an accurate description can miss the question the user needs answered.
Why do people disagree about whether an explanation is understandable?
Comprehension is not a property that can be reliably inferred from wording alone. In a small 2021 NIST pilot, six judges rated the comprehensibility of textual-entailment justifications. NIST reported low interrater agreement, with an intra-class correlation of about 0.4. More than half of the explanations received both a “Very Poor” or “Poor” rating and a “Good” or “Very Good” rating from different judges; in 32 cases, the same explanation received all five possible ratings.
This pilot is limited evidence: it involved six judges and one kind of explanation, so it should not be generalized to all users or AI explanations. It does, however, illustrate why a team should test its intended explanations with the people expected to use them rather than assume that an internal review establishes comprehensibility.
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How should teams evaluate an AI explanation?
Evaluation should name both the intended user and the task. Asking whether people “like” an explanation or say they trust it is not enough: perceived clarity does not establish that the account is faithful, and perceived trust does not show that the explanation helped someone make a better-informed decision.
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Measure explanation quality in context
Ask whether the intended users find the explanation understandable, useful, actionable, sufficient, appropriately concise, trustworthy, correct, and easy to use for the specific decision. These qualities may pull in different directions: a compact explanation can omit detail a reviewer needs, while a detailed one can overwhelm someone seeking a direct answer.
Measure effects on interaction
Check whether the explanation changes how people understand or interact with the system. Relevant questions include whether it affects perceived trust or control, cognitive demand, confidence, or willingness to use the system. These outcomes describe the interaction; they do not, by themselves, demonstrate that the explanation faithfully reflects model behavior.
Measure effects on task performance
Test whether users can perform the task better or discover useful insights with the explanation than without it. For a reviewer, that might mean being able to identify a recommendation that merits further checking. The evaluation should measure the real task rather than substitute a general satisfaction rating for evidence of usefulness.
Check fidelity and reliability separately
Assess whether the explanation reflects the system’s process and whether it remains consistent, clear, and robust in the conditions where it will be used. NIST guidance recommends involving relevant actors and end users before deployment and examining properties including fidelity, ambiguity, consistency, resilience, and interpretability, as well as clarity, accuracy, and understandability.
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What does the research say about evaluation methods?
A 2024 systematic review examined 73 papers evaluating explainable-AI explanations with users. It identified 30 components of meaningfulness, grouped around contextual explanation quality, contributions to human–AI interaction, and contributions to human–AI performance. Only 19 of the 73 papers used an evaluation framework that at least one other study in the sample also used.
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Those counts describe the review’s selected literature, not every XAI study or a timeless census of the field. They nevertheless show why results can be difficult to compare: studies may use different measures and ask different questions about what makes an explanation meaningful. A clear evaluation report should state who the participants were, what task they performed, which explanation properties were assessed, and whether the study measured user perception, task outcomes, or fidelity to the system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a practical explanation design process look like?
- Define the decision and audience. Identify who will use the explanation, what they already know, and what action or judgment it should support.
- Choose the explanation approach for the setting. NIST points to inherently explainable model families as one possible approach and also recommends testing post-hoc explanations. The guidance does not establish a universal ranking; either choice still needs evaluation for accuracy and comprehensibility in its intended use.
- State what the explanation does and does not establish. Make clear whether it describes the system’s process, provides context for an output, or supports a user’s next step. Do not imply that a plausible explanation is proof that a decision was correct.
- Test with the people and task that matter. Seek feedback from relevant actors and end users before deployment. Measure whether the explanation is understood, faithful to system behavior, useful in interaction, and helpful for performance.
- Review the explanation when the system or context changes. If the model, task, audience, or conditions of use change, the earlier explanation evaluation may no longer answer the relevant questions. Reassess the properties that matter in the updated setting.
Why the explanation gap matters
The central problem is not simply that AI uses technical language. It is that an explanation has to bridge the system’s behavior, a person’s perspective, and a real decision. NIST’s principles make user meaningfulness and accurate representation separate requirements, while user-study evidence shows that evaluations of meaningfulness vary widely. Closing the gap therefore requires more than making explanations sound human: teams must establish that the account is faithful, understandable to the intended people, and useful for the task.
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