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AI Product Tools / MIF Explorer / Library / AI
Truth Layer
DirectionalLeadingAI-Sensitive Truth Layer
The Truth Layer is the badge system that tells you how trustworthy, directional, or risky a measure is.
Why it matters: It helps teams separate meaningful signals from vanity, misuse, or AI distortion before they optimize the wrong thing.
Example: A metric can be Meaningful, Leading, or Vanity Risk.
AI Suggestion Acceptance Rate
The percentage of AI-generated suggestions that users accept and apply.
Evaluation method
accepted_suggestions / total_suggestions_shown × 100
Signal type
leading
What it is best for
Evaluating AI suggestion quality
What it tells you +
Whether the AI’s suggestions are relevant, trustworthy, and useful to users.
What it does not tell you +
Tell you whether accepted suggestions led to good outcomes or whether users understood what they accepted.
When to use it +
- Evaluating AI suggestion quality
- Identifying which types of suggestions users find most valuable
- Measuring the impact of AI model improvements
When not to use it +
- As a standalone quality metric without checking downstream outcomes
How leaders misuse it +
- Treating high acceptance as proof of quality when users may be accepting defaults without review
- Optimizing for acceptance rate rather than outcome quality
Anti-patterns +
- Making suggestions hard to dismiss to inflate acceptance rates
AI interpretation risks +
Scenario: AI suggestions are the default option
What happens: Acceptance rate is artificially high because users rarely change defaults
What it really means: High acceptance may reflect default bias, not genuine preference or trust.
Recommendation: A/B test with non-default suggestions. Compare deliberate accepts vs passive non-rejection.
Companion entries +
This entry is stronger when paired with:
Instrumentation or evaluation guidance +
Track suggestion type, context, and user segment. Distinguish explicit accepts from passive defaults.
Sample events
suggestion_shown, suggestion_accepted, suggestion_dismissed Examples +
An AI email composer shows 72% suggestion acceptance. However, emails using AI suggestions receive 15% lower reply rates, suggesting the suggestions are convenient but not high quality.
Suggested decisions +
- If acceptance rate is above 80%, check whether users are reviewing suggestions or blindly accepting
- If acceptance rate is below 30%, investigate suggestion relevance and timing