AI Risk Tiering is categorizing AI use cases by risk level. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0025, within the Risk family. The core principle: categorizing AI use cases by risk level. 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
Categorizing AI use cases by risk level.
Plain-English Definition
Categorizing AI use cases by risk level.
Feynman Explanation
Not every model needs the same governance.
Core Principle
Categorizing AI use cases by risk level.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Not every model needs the same governance.
Examples
- EU AI Act risk categorization.
- Governance proportional to risk.
Pending editorial review.
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: not every model needs the same governance. 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, governance proportional to risk. It is amplified whenever governance proportional to risk. Inside organizations that shows up as governance proportional to risk. 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 tier your AI portfolio. Govern accordingly. Treat it as infrastructure. Once you can see it in your own system, most of the argument about culture resolves itself.
Famous Experiments
Pending editorial review.
Design Principles
- Tier your AI portfolio. Govern accordingly.
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.
- Tier your AI portfolio. Govern accordingly.
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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?
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.
Adversarial testing of AI systems.
Foundational skills erode through AI offloading.
Concentration risk on a single AI provider.
Where AI Risk Tiering 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.
- EssayIncentives Under Crisis
How this element behaves under pressure.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
Questions about AI Risk Tiering
- What is AI Risk Tiering?
- AI Risk Tiering is categorizing AI use cases by risk level. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0025, within the Risk family. The core principle: categorizing AI use cases by risk level. 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 AI Risk Tiering?
- Governance proportional to risk. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0025).
- How is AI Risk Tiering exploited?
- Governance proportional to risk.
- How do you design around AI Risk Tiering?
- Tier your AI portfolio. Govern accordingly.
- Which behavioral dimension does AI Risk Tiering belong to?
- AI Risk Tiering is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Risk", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0025 and its evidence grade is C.