Red-Teaming AI is adversarial testing of AI systems. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0231, within the Risk family. The core principle: adversarial testing of AI systems. 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
Adversarial testing of AI systems.
Plain-English Definition
Adversarial testing of AI systems.
Feynman Explanation
Pay someone to break it before users do.
Core Principle
Adversarial testing of AI systems.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Pay someone to break it before users do.
Examples
- Standard practice for major model launches.
- Pre-deployment risk reduction.
Pending editorial review.
Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
Executives usually notice this element only after it has cost something. By then it looks like a one-off. It is not. 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: pay someone to break it before users do. 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, pre-deployment risk reduction. It is amplified whenever pre-deployment risk reduction. Inside organizations that shows up as pre-deployment risk reduction. 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 internal and external red-teaming. Continuous, not one-off. 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
- Internal and external red-teaming. Continuous, not one-off.
Measurement Approaches
Pending editorial review.
Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Internal and external red-teaming. Continuous, not one-off.
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Pending editorial review.
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.
Adding noise to data to protect individual privacy.
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.
Foundational skills erode through AI offloading.
Concentration risk on a single AI provider.
Where Red-Teaming AI 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.
- EssayThe Comp Plan Is the Strategy
Where this element meets compensation design.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
Questions about Red-Teaming AI
- What is Red-Teaming AI?
- Red-Teaming AI is adversarial testing of AI systems. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0231, within the Risk family. The core principle: adversarial testing of AI systems. 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 Red-Teaming AI?
- Pre-deployment risk reduction. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0231).
- How is Red-Teaming AI exploited?
- Pre-deployment risk reduction.
- How do you design around Red-Teaming AI?
- Internal and external red-teaming. Continuous, not one-off.
- Which behavioral dimension does Red-Teaming AI belong to?
- Red-Teaming AI is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Risk", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0231 and its evidence grade is C.