Concern 197 · Working draft · editorial review in progress
Explanation quality across users
Explanation quality across users concerns whether different people receive explanations they can understand and use.
Understanding the concern
An engineer may need diagnostic detail, while an applicant may need to know what information was used and how to correct it. A regulator may need evidence about the process across many cases. Giving everyone the same technical chart can fail all three audiences.
Plain language alone is not enough if it omits the information needed for action. Explanations should match the recipient's question without changing the facts. Testing them with intended readers can reveal whether they support understanding, challenge, and informed choice.
An example
A mechanic and a driver need different views of the same repair problem, but both need an accurate account.
Illustrative unless identified as a documented case.
Evidence and its limits
Both sources emphasize meaningful, audience-sensitive explanations; the mechanic analogy is illustrative.
NIST · IR 8312 · 2021 · Official research framework
Four Principles of Explainable Artificial Intelligence ↗ (opens in a new tab)Distinguishes explanation, meaningfulness, explanation accuracy and knowledge limits; these are separate evaluation questions.
Information Commissioner's Office and Alan Turing Institute · Official guidance
Explaining decisions made with AI ↗ (opens in a new tab)Explains audience-sensitive accounts of AI-assisted decisions; legal provisions must be checked against current jurisdiction and context.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Can the person receiving this explanation use it to take the next relevant step?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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