Trust Calibration is matching trust in a system to its actual reliability. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0278, within the Trust family. The core principle: matching trust in a system to its actual reliability. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
Matching trust in a system to its actual reliability.
Plain-English Definition
Matching trust in a system to its actual reliability.
Feynman Explanation
Too much trust ships errors. Too little wastes capability.
Core Principle
Matching trust in a system to its actual reliability.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Too much trust ships errors. Too little wastes capability.
Examples
- Users either over-trusting or rejecting AI outputs.
- Adoption strategy and risk management.
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Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This element is common enough to feel like human nature and specific enough to be engineered around. The mechanism underneath it operates in the Incentives dimension — what makes behavior more or less likely?. You can recognize it in the field by its signature: too much trust ships errors. Too little wastes capability. Every element in the Incentives dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, adoption strategy and risk management. It is amplified whenever adoption strategy and risk management. Inside organizations that shows up as adoption strategy and risk management. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to surface confidence intervals. Train users on appropriate skepticism. Measure the behavior, not the sentiment. A survey will tell you how people feel about this; only observed action tells you whether it changed.
Famous Experiments
Pending editorial review.
Design Principles
- Surface confidence intervals. Train users on appropriate skepticism.
Measurement Approaches
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Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Surface confidence intervals. Train users on appropriate skepticism.
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Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
Treating AI as more humanlike than it is.
Reduced vigilance with automated systems.
Quantifying uncertainty in model outputs.
Confident outputs that are factually wrong.
Written rules about how AI may be used internally.
Innovators → early adopters → majority → laggards, AI-specific.
AI system that takes actions to achieve goals, often across tools.
When the agent acts, who's responsible?
Augmentation strategy vs. substitution strategy.
Logging of AI inputs, outputs, and decisions.
Inventory of models, data, tools, and dependencies in an AI system.
Tracing AI components for risk and compliance.
Where Trust Calibration is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- Field guideIncentives: definition, types, examples
The parent field guide for this element.
- ReferenceThe laws of incentives
Goodhart, Campbell, and the Cobra Effect.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Trust Calibration
- What is Trust Calibration?
- Trust Calibration is matching trust in a system to its actual reliability. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0278, within the Trust family. The core principle: matching trust in a system to its actual reliability. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of Trust Calibration?
- Adoption strategy and risk management. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0278).
- How is Trust Calibration exploited?
- Adoption strategy and risk management.
- How do you design around Trust Calibration?
- Surface confidence intervals. Train users on appropriate skepticism.
- Which behavioral dimension does Trust Calibration belong to?
- Trust Calibration is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Trust", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0278 and its evidence grade is C.