Protocol
Test actual skill before letting perceived expertise override AI advice
Self-confidence is useful only when it tracks competence.
When it fits
- A user rejects AI assistance because they feel expert, or accepts it because they feel inexperienced.
When to avoid it
- Expertise can include contextual knowledge that benchmark cases miss; combine performance evidence with domain judgment.
Why it matters
For repeated task classes, compare the person's unaided performance and confidence before using self-perceived expertise as a routing signal. High verified skill can justify more human autonomy; high self-confidence without discrimination should not automatically suppress useful AI checks.
Steps
- Unaided performance is measured on representative cases.
- Self-confidence is recorded separately.
- Confidence-accuracy alignment is inspected.
- Routing rules distinguish verified skill from self-labels.
- Performance is rechecked as the task changes.
An example
A senior analyst who feels expert in a new vendor API should still benchmark unaided accuracy before rejecting all tool support.
Check your result
The reliance policy responds to demonstrated task competence rather than job title or subjective expertise alone.
Keep this limit in mind
- Expertise can include contextual knowledge that benchmark cases miss; combine performance evidence with domain judgment.
Evidence and sources
A 2026 two-study paper found trust predicted both consultation and adoption of ChatGPT input, while perceived expertise reduced reliance even when perceived expertise did not necessarily equal actual skill.
The relationship between perceived expertise and true competence is task-specific.
Who listens to ChatGPT and when should they? A two-study examination of AI-assisted decision making · Abstract highlights
A 2026 study found metacognitive estimates of one's own confidence shape responses to AI advice and limited metacognitive sensitivity can produce inconsistent advice-taking.
Detailed boundary conditions should be interpreted from the full study rather than the abstract alone.
AI advice and human metacognition · Abstract highlights