Scientific Definition
Quantifying uncertainty in model outputs.
Plain-English Definition
Quantifying uncertainty in model outputs.
Feynman Explanation
An answer without uncertainty is a guess wearing a confidence costume.
Core Principle
Quantifying uncertainty in model outputs.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
An answer without uncertainty is a guess wearing a confidence costume.
Examples
- Probabilistic forecasting with quantified ranges.
- Decision quality under model uncertainty.
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Famous Experiments
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Design Principles
- Require uncertainty quantification on every model output that drives decisions.
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.
- Require uncertainty quantification on every model output that drives decisions.
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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.
Confident outputs that are factually wrong.
Matching trust in a system to its actual reliability.
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.