What it means
Explainability covers citations, chain-of-thought summaries, highlight-to-source links, confidence labels, and plain-language “why this recommendation” copy.
Why designers should care
When explainability is missing, users treat fluent text as truth; when it is cluttered, they ignore it. The job is calibrated disclosure for the task risk level.
Example
A loan review assistant shows three cited policy clauses, a one-line rationale, and a “See full reasoning” expander auditors can export, while shoppers see only a short summary.
Common mistakes
- Fake explainability: generic “Based on your data” with no actual source links.
- Dumping raw model reasoning on every user regardless of task sensitivity.
- Explainability that disappears when the model is wrong, eroding trust further.
Frequently asked questions
What is Explainability?
Explainability is how clearly an AI product shows why it produced an answer: sources, reasoning steps, confidence, or feature influence.
Why should designers care about Explainability?
When explainability is missing, users treat fluent text as truth; when it is cluttered, they ignore it. The job is calibrated disclosure for the task risk level.
What are common mistakes with Explainability?
Fake explainability: generic “Based on your data” with no actual source links; Dumping raw model reasoning on every user regardless of task sensitivity; Explainability that disappears when the model is wrong, eroding trust further.