Acceptable Use Policy (AI) is written rules about how AI may be used internally. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0004, within the Governance family. The core principle: written rules about how AI may be used internally. 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
Written rules about how AI may be used internally.
Plain-English Definition
Written rules about how AI may be used internally.
Feynman Explanation
Without a policy, every team writes their own. Inconsistently.
Core Principle
Written rules about how AI may be used internally.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Without a policy, every team writes their own. Inconsistently.
Examples
- Use policies covering PII, IP, sensitive data, attribution.
- Necessary infrastructure for scaled adoption.
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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
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: without a policy, every team writes their own. Inconsistently. 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, necessary infrastructure for scaled adoption. It is amplified whenever necessary infrastructure for scaled adoption. Inside organizations that shows up as necessary infrastructure for scaled adoption. 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 write it. Train on it. Audit against it. Treat it as infrastructure. Once you can see it in your own system, most of the argument about culture resolves itself.
Famous Experiments
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Design Principles
- Write it. Train on it. Audit against it.
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.
- Write it. Train on it. Audit against it.
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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.
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.
Cross-functional governance body for AI decisions.
Agents deployed before anyone owns the consequences.
Cryptographic tracking of content origin.
Tracking where training and inference data came from.
Comprehensive AI regulation in the EU.
Why did it produce this? vs. How does it work?
Human review at critical AI decision points.
Human oversight without per-decision review.
Documentation of model purpose, performance, limitations, and risks.
Where Acceptable Use Policy (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.
- EssayWhy Government Transformation Stalls
The public-sector version of this pattern.
- 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 Acceptable Use Policy (AI)
- What is Acceptable Use Policy (AI)?
- Acceptable Use Policy (AI) is written rules about how AI may be used internally. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0004, within the Governance family. The core principle: written rules about how AI may be used internally. 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 Acceptable Use Policy (AI)?
- Necessary infrastructure for scaled adoption. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0004).
- How is Acceptable Use Policy (AI) exploited?
- Necessary infrastructure for scaled adoption.
- How do you design around Acceptable Use Policy (AI)?
- Write it. Train on it. Audit against it.
- Which behavioral dimension does Acceptable Use Policy (AI) belong to?
- Acceptable Use Policy (AI) is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Governance", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0004 and its evidence grade is C.