AI Explainability Requirements
Explainability is the product's answer to "why did the AI do that?" Users who understand why an AI made a recommendation are more likely to trust it, catch its errors, and use it effectively. Regulators and enterprise buyers increasingly require it. This skill defines the explainability requirements for a feature and the product design that delivers them.
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Context
The three levels of explainability:| Level | What it provides | Who needs it |
|---|---|---|
| Output-level | "Here's what the AI produced and a confidence indicator" | All users |
| Decision-level | "Here's why the AI made this recommendation" | Users affected by AI decisions; regulated contexts |
| Audit-level | "Here's a full record of the AI's inputs, reasoning, and outputs" | Enterprise buyers; compliance teams |
The most accurate AI models are often the least interpretable. The PM must decide if the accuracy benefit is worth the explainability cost.
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Step 1 — Define the explainability requirements
Ask: what is the AI deciding, who is affected, regulatory context, what users need to use the output responsibly, what they need to challenge it, what auditors need.
Step 2 — Select the explainability level
Output-level (minimum for all AI features), Decision-level (High/Critical stakes), or Audit-level (regulated/enterprise contexts).
Step 3 — Design the explanation content
For recommendations: top 3 factors in plain language + counter-factual.
For classifications: result + primary evidence + what would change it.
For generative features: source citations + confidence indicators.
Step 4 — Design the explanation UX
Patterns: simple reason tag, ranked factors, confidence + source, or decision audit panel. Plus challenge/override UI for all decision-level features.
Step 5 — Define the audit log requirements
For audit-level: full log schema including model version, input hash, output, explanation, human reviewer actions, user challenges. Retention and access controls.