AI Bill of Materials is inventory of models, data, tools, and dependencies in an AI system. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0018, within the Governance family. The core principle: inventory of models, data, tools, and dependencies in an AI system. 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
Inventory of models, data, tools, and dependencies in an AI system.
Plain-English Definition
Inventory of models, data, tools, and dependencies in an AI system.
Feynman Explanation
You can't govern what you can't list.
Core Principle
Inventory of models, data, tools, and dependencies in an AI system.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
You can't govern what you can't list.
Examples
- Increasingly required by regulators and enterprise procurement.
- Supply chain transparency for AI systems.
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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
When this element shows up in a diagnostic, the instinct is to train people out of it. Training rarely moves it. 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: you can't govern what you can't list. 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, supply chain transparency for AI systems. It is amplified whenever supply chain transparency for AI systems. Inside organizations that shows up as supply chain transparency for AI systems. 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 maintain AI-BOMs. Update them. Use them for risk review. The test of any redesign here is simple: after the change, can you name what the organization is now doing less of? If not, the payoff structure did not actually move.
Famous Experiments
Pending editorial review.
Design Principles
- Maintain AI-BOMs. Update them. Use them for risk review.
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.
- Maintain AI-BOMs. Update them. Use them for risk review.
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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.
Tracing AI components for risk and compliance.
Written rules about how AI may be used internally.
Logging of AI inputs, outputs, and decisions.
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 AI Bill of Materials 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.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about AI Bill of Materials
- What is AI Bill of Materials?
- AI Bill of Materials is inventory of models, data, tools, and dependencies in an AI system. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0018, within the Governance family. The core principle: inventory of models, data, tools, and dependencies in an AI system. 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 Bill of Materials?
- Supply chain transparency for AI systems. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0018).
- How is AI Bill of Materials exploited?
- Supply chain transparency for AI systems.
- How do you design around AI Bill of Materials?
- Maintain AI-BOMs. Update them. Use them for risk review.
- Which behavioral dimension does AI Bill of Materials belong to?
- AI Bill of Materials is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Governance", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0018 and its evidence grade is C.