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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhen an AI makes a consequential recommendation, people naturally ask, “Why did you do that?” A system may return a technically plausible explanation and still fail to answer the question that matters to the person using—or affected by—it. A useful explanation must reflect how the system actually worked, make sense to its intended audience, and help with the decision at hand. Those are separate requirements, and meeting one does not guarantee the others.
What does it mean for AI to explain a decision?
“Explainable AI” can sound like a single capability: the system gives a reason for its output. In practice, the word “explanation” covers several different questions. NIST’s AI Risk Management Framework resource separates three related ideas:
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- Transparency: What happened?
- Explainability: How did the system arrive at its decision?
- Interpretability: Why does that decision mean what it does in this context, given the system’s intended function?
These distinctions matter. A record showing which data went into a model may make its operation more transparent without explaining how those inputs led to a result. A description of that process may still leave a caseworker or an affected person unsure what the result means for their situation.
There is also a difference between an explanation’s faithfulness and its clarity. Faithfulness asks whether the explanation accurately represents the process that produced the output. Clarity asks whether a particular person can understand it. An explanation can be readable but misleading about the model’s behavior; a faithful account can be so technical or context-free that it does not help its audience.
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Why can’t AI explain its decisions in plain language?
There is no single human audience and no single decision context. The explanation useful to a data scientist investigating model behavior may not be the one a caseworker needs to review a recommendation—or the one a person needs to understand how a decision affects them. NIST guidance says descriptions can be tailored to a user’s role, knowledge, and skill.
One output, different information needs
Consider an AI system that recommends prioritizing an application for review. A technical reviewer may need evidence about the model’s inputs and behavior. The staff member responsible for acting on the recommendation may need a concise, relevant reason and a way to check it. The applicant may need to understand the decision’s personal significance. A single explanation written for all three risks being either too general to help or too technical for some of them.
NIST’s 2020 article on its proposed explainable-AI principles makes this point directly. Jonathon Phillips, an NIST electronic engineer and one of the report’s authors, said: “But an explanation that would satisfy an engineer might not work for someone with a different background. So, we want to refine the draft with a diversity of perspective and opinions.” The statement captures a design problem, not merely a writing problem: selecting what to show depends on who needs to use the explanation and why.
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Translating a model output into smooth prose does not establish that the prose is a faithful account of the model’s behavior. Conversely, a feature attribution or technical trace may accurately describe part of a model’s operation without conveying what a person should infer from it. Treating readability as a substitute for accuracy—or technical detail as a substitute for usefulness—confuses different tests.
People do not always agree on what is understandable
A small NIST pilot study illustrates how subjective judgments of comprehensibility can be. In 2021, six judges rated 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, from “Very Poor” through “Very Good.”
This was a pilot involving six judges and one kind of explanation—not a representative survey of AI explanations or users. It does, however, show why designers should not assume that one person’s judgment of clarity represents everyone who must rely on an explanation.
What principles should a useful explanation meet?
NIST’s 2020 draft report proposed four principles for explainable AI. They are a framework for thinking about explanation quality, not a claim that every system can satisfy them in the same way:
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- Be meaningful to individual users. NIST’s summary says, “Systems should provide explanations that are meaningful or understandable to individual users.” What counts as meaningful depends on the audience and task.
- Correctly reflect the process that generated the output. A persuasive explanation that does not match the system’s actual behavior is not a sound explanation.
- Operate within designed conditions or with sufficient confidence. An explanation should not imply certainty or reliable operation beyond the conditions the system was designed to handle.
These principles expose the central tension: an explanation needs to be understandable without distorting what happened. Adding more detail may improve technical scrutiny but overwhelm a user; simplifying may help a user follow the account but risks concealing important qualifications. The right balance depends on the system, audience, and decision.
How do you know whether an AI explanation is understandable?
Ask who needs the explanation and what they need to do with it. Then evaluate several distinct outcomes instead of treating a favorable reaction as proof that the explanation works. A 2024 systematic review of 73 papers evaluating explainable-AI explanations with users identified 30 components of meaningfulness, grouped into three broad dimensions:
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| Evaluation dimension | What to examine | Example question |
|---|---|---|
| In-context explanation quality | Whether users find an explanation understandable, useful, actionable, sufficient, compact, trustworthy, correct, or easy to use. | Can this person understand the explanation well enough to check or use it for this task? |
| Contribution to human-AI interaction | Whether the explanation affects understanding of the system, perceived trust or control, cognitive demand, confidence, or willingness to use it. | Does it change how the person interacts with the system, and in what way? |
| Contribution to human-AI performance | Whether the explanation helps people perform the task or discover insights. | Does it help the person make a better-informed decision or complete the task? |
The review’s figures describe the selected studies and methods it examined, not a census of all XAI research. It also found that only 19 of the 73 papers used an evaluation framework that appeared in at least one other paper in its sample. The varied approaches make results harder to compare and underline why “users liked the explanation” cannot stand in for a fuller evaluation.
Test perceived clarity and technical fidelity separately
Ask intended users whether they understand the explanation, but also test whether it accurately represents the system’s behavior. A user’s satisfaction or trust is evidence about their reaction; by itself, it does not prove the explanation is faithful. Likewise, a technically faithful explanation does not prove that the user can apply it correctly.
Measure the task, not just the explanation
Decide in advance what the person should be able to do: check a recommendation, identify a relevant factor, challenge an error, or complete a decision accurately. Evaluate whether the explanation supports that task and whether it changes the person’s understanding or performance. A system can improve perceived confidence without improving performance, so those outcomes should not be collapsed into one score.
Include the people who will encounter the system
NIST recommends seeking feedback before deployment from relevant actors and end users, and assessing clarity, accuracy, and understandability. That means including the people expected to operate, oversee, or be affected by the system—not only the team that built it. The test should reflect the setting in which the explanation will be used, because an explanation that works in a controlled demonstration may not resolve the questions raised during an actual decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do explainable models solve the communication problem?
Not by themselves. NIST guidance discusses inherently explainable model families as one possible approach and also recommends testing post-hoc explanations—accounts generated to describe a model’s output after the model has made it. The available guidance does not establish a universal ranking in which one approach is always better.
Whichever approach is chosen, assess both whether the explanation accurately reflects the system and whether the intended user can understand and use it in context. A model or method can make evidence easier to inspect, but that alone does not guarantee the explanation answers the user’s question or supports the task. The evaluation should cover properties such as fidelity, consistency, robustness, ambiguity, and interpretability as well as user-facing clarity.
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What the explanation gap means in practice
The gap is not simply that AI needs better wording. It is a mismatch among the system’s actual behavior, the explanation method, and the role and task of the person receiving the explanation. NIST’s principles and applied guidance point toward a practical standard: an explanation should give relevant reasons, represent the system accurately, fit its audience, and be tested with the people and decisions it is meant to serve.
No one quality—plain language, user trust, technical detail, or a positive rating—answers all of those questions. Treating them as separate evaluation goals makes it possible to see where an explanation helps, where it misleads, and where it simply fails to answer the question being asked.
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