Concern 196 · Working draft · editorial review in progress
Explanations under privacy constraints
Explanation under privacy constraints means explaining an AI decision without unnecessarily exposing someone else's personal information or protected material.
Understanding the concern
An affected person may need to know why a decision was made. At the same time, the supporting data can contain information about other people, confidential records, or sensitive attributes. Publishing all of it may create a new harm rather than a useful explanation.
Privacy is not a blanket reason to provide no explanation. A system can often explain relevant factors, decision procedures, and routes to correction without revealing individual records. The right approach depends on the decision, the audience, and applicable rules; sensitive features are not automatically forbidden to discuss.
An example
A school explains why an application was declined without revealing another student's private file.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The guidance discusses explanations for affected people and contextual constraints. It does not create a universal rule barring disclosure of protected attributes.
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.
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.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
What can we explain meaningfully while protecting information that the recipient has no need to see?
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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