Dynamic Trust Calibration
The Principle
Trust in AI systems should evolve through interaction, not be set statically at deployment. Systems should start with limited autonomy and gradually expand as the AI demonstrates reliability, a pattern of “trust but verify” that builds confidence through evidence.
Why This Matters
Static trust assumptions create two failure modes:
Overtrust – Assuming AI is reliable before it has demonstrated reliability leads to automation bias, missed errors, and inappropriate delegation of critical decisions.
Undertrust – Assuming AI is unreliable when it has demonstrated capability leads to underutilization, wasted human attention on tasks AI handles well, and failure to capture automation benefits.
Dynamic calibration avoids both by making trust earned rather than assumed.
How to Apply
- Start conservative – New AI systems begin at Level 1 autonomy (decision support) with full human-in-the-loop verification
- Accumulate evidence – Track AI performance across decisions. Surface this track record to users.
- Graduate responsibility – As accuracy is confirmed, allow progression to higher autonomy levels for well-understood task types
- Maintain escape valves – Even at high autonomy, preserve human override capabilities and audit trails
- Downgrade on failure – When AI fails, reduce autonomy and require re-earning of trust
Real-World Pattern
In the CyberAlly deployment study, analysts initially verified every AI-flagged alert manually. Over time, as CyberAlly proved accurate in classifying phishing emails and triaging anomalies, analysts shifted to only reviewing AI-escalated items. The AI earned its way from Level 1 to Level 2-3 autonomy through demonstrated performance.
When This Especially Matters
- High-stakes decisions where errors are costly
- Novel AI systems without established track records
- Domains where AI capabilities are rapidly evolving
- Situations where human trust is initially low due to past AI failures
Related: 05-molecule—triadic-human-ai-model, 05-atom—three-hitl-configurations