Scientific Definition
Adding noise to data to protect individual privacy.
Plain-English Definition
Adding noise to data to protect individual privacy.
Feynman Explanation
Useful patterns without identifiable people.
Core Principle
Adding noise to data to protect individual privacy.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Useful patterns without identifiable people.
Examples
- Apple's iOS data collection.
- Privacy-preserving analytics and model training.
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Famous Experiments
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Design Principles
- Use where regulatory or trust pressure justifies the accuracy tradeoff.
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.
- Use where regulatory or trust pressure justifies the accuracy tradeoff.
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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.
When the agent acts, who's responsible?
Categorizing AI use cases by risk level.
Systematic skew in model behavior across groups.
Testing model behavior on hypothetical alternate inputs.
Quantitative measures of model behavior across groups.
Training models across devices without centralizing data.
Bypassing model safety constraints.
Individual speed gains hide collective quality decline.
Malicious instructions hidden in user input or retrieved content.
Adversarial testing of AI systems.
Foundational skills erode through AI offloading.
Concentration risk on a single AI provider.