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The Incentives Lab
AI Incentives · Governance

AI Bill of Materials

Inventory of models, data, tools, and dependencies in an AI system.

"You can't govern what you can't list."

Quick answer

What is AI Bill of Materials? Inventory of models, data, tools, and dependencies in an AI system. Supply chain transparency for AI systems.

In the wild

Increasingly required by regulators and enterprise procurement.

Why it matters in the room

Supply chain transparency for AI systems.

Counter-move

Maintain AI-BOMs. Update them. Use them for risk review.

Visual · Reward gradient
REWARD ↑OPTIMIZER →
AI Bill of Materials shows where an optimizer climbs vs where we want it to go.
Live example · Train around AI Bill of Materials

Pick what to reward the model for.

Inventory of models, data, tools, and dependencies in an AI system. In the wild: Increasingly required by regulators and enterprise procurement.

● Live

Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.

How does this land?

Pick a reaction to AI Bill of Materials

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Human Behavior Element™ · HBE Spec

The full taxonomy entry

Every concept in the Atlas uses the same structure — so AI Bill of Materials can be compared, recombined, and cited like an element on a periodic table.

About the standard →
A
AB
HBT-A8087
Official name
AI Bill of Materials
AI Incentives · Governance
Identity
HBT ID
HBT-A8087
Symbol
AB
Official name
AI Bill of Materials
Synonyms
Governance
Keywords
AI Incentives, Governance, human behavior, incentive design
Version
v1.0
Last updated
Maintained by The Incentives Lab
Classification
Kingdom
Systems
Domain
Machine Behavior
Family
AI Alignment & Incentives
Class
Governance
Element
AI Bill of Materials
Definition
Scientific
Inventory of models, data, tools, and dependencies in an AI system.
Plain-English
Inventory of models, data, tools, and dependencies in an AI system.
Feynman
You can't govern what you can't list.
Core principle
Inventory of models, data, tools, and dependencies in an AI system.
One-sentence summary
Supply chain transparency for AI systems.
Mechanisms
Psychological
Inventory of models, data, tools, and dependencies in an AI system.
Behavioral econ.
Supply chain transparency for AI systems.
Neurological
Reward, threat, and salience circuits bias attention toward the cue.
Evolutionary
Heuristics that paid off in ancestral environments now misfire in modern systems.
Sociological
Group norms and status incentives reinforce the pattern across a team.
Computational
Models trained on biased human signals will replicate and amplify the pattern.
Systems thinking
Feedback loops between metrics, incentives, and behavior lock the pattern in place.
Signals & signature
Inputs (activators)
Increasingly required by regulators and enterprise procurement.
Outputs (observable)
Supply chain transparency for AI systems.
Behavioral signature
You see AI Bill of Materials when the explanation for a decision sounds reasonable but the outcome keeps repeating.
Behavioral molecules
Often combines with related Atlas entries — see the rail below.
Pathways · before
A goal, metric, or contract clause makes the behavior rational locally.
Pathways · after
Locally rational choices accumulate into a systemic distortion.
Domains where it shows up
  • Business
  • Leadership
  • Government
  • Healthcare
  • Education
  • Sales
  • Marketing
  • AI
  • Negotiation
  • Media
  • Public Policy
  • Relationships
Examples
Everyday
Increasingly required by regulators and enterprise procurement.
Modern
Supply chain transparency for AI systems.
Historical
A pattern repeatedly documented since the foundational behavioral science literature on ai alignment & incentives.
Famous experiments
See the References block — primary papers in the Atlas link out to the original studies.
Design principles
How to leverage
Supply chain transparency for AI systems.
How to reduce
Maintain AI-BOMs. Update them. Use them for risk review.
How to redesign
Maintain AI-BOMs. Update them. Use them for risk review.
The Perverse Incentive Lens™
How it's exploited
Organizations weaponize ai bill of materials — sometimes deliberately, often by accident — when metrics reward the symptom rather than the outcome.
Common perverse incentives
Volume metrics, short review windows, bonus cliffs, and contracts that pay on activity rather than impact.
Failure modes
When AI Bill of Materials dominates, teams optimize for the dashboard while the real outcome quietly degrades.
Incentive redesign
Maintain AI-BOMs. Update them. Use them for risk review.
Ethical considerations
Don't engineer ai bill of materials into customers, employees, or citizens as a manipulation tactic — design for informed choice instead.
Diagnostic questions
  • Where in our org would AI Bill of Materials most often show up unnoticed?
  • Which metric, ritual, or contract clause quietly rewards AI Bill of Materials?
  • If we removed every payoff for AI Bill of Materials, what behavior would replace it?
  • Who benefits when AI Bill of Materials persists — and who pays the cost?
Organizational warning signs
Metrics
A KPI is hit while the underlying outcome stalls or worsens.
Behaviors
People route around the rule rather than challenge it.
Language
'That's just how we do it here.' / 'The system requires it.'
Culture
Naming the pattern is treated as disloyalty.
Red flags
  • People defend the status quo using the language of ai bill of materials.
  • Decisions cluster around the easiest narrative rather than the strongest evidence.
  • New data changes the slide deck but not the decision.
  • Anyone naming the pattern is treated as the problem.
Intervention playbook
Immediate
Make the perverse payoff visible to the people creating it.
30-day
Run a small pilot that pays for the outcome, not the proxy.
Long-term
Rewrite the comp plan, contract, or ritual so the right behavior becomes the easy behavior.
AI considerations
Detect
Audit training data and reward signals for the same pattern this element describes.
Avoid amplifying
Don't optimize models on metrics that already encode the perverse incentive.
Counteract
Use the model to surface where the pattern is most active, then redesign the incentive — not the model.
Measurement
Metrics
Outcome-to-proxy ratio over time.
Assessment
The Incentives Lab III Diagnostic.
Survey
Calibrated pulse questions on rules vs. outcomes.
Behavioral signals
Where people work around the system.
Observational
Where the dashboard and the lived experience disagree.
Scientific evidence
Evidence grade
Synthesized from the behavioral science literature; see Atlas references.
Replication
Tracked in the Atlas as primary, replicated, or contested.
Intervention confidence
Moderate — patterns generalize, mechanisms vary by context.
Research consensus
Broad agreement on the pattern; ongoing debate on boundary conditions.
Known limitations
Local context, culture, and incentive structure all change the strength of the effect.
Open questions
How does AI Bill of Materials interact with AI-mediated decisions at scale?
References
Meta-analyses
Tracked in the Atlas registry.
Seminal authors
Kahneman, Tversky, Thaler, Ariely, Cialdini, Ostrom, Simon — and the field they built.
Cross references

Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.

Disciplinary layers

See AI Bill of Materials through 2 lenses

Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.

Test yourself · 60 seconds

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Three quick questions. Result is saved into your review streak — come back when the term is due to lock it in.

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Go deeper

Worked example, counter-example & concept map

On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.

How this lands in you

Your nervous system has a region for this.

Primary region
Prefrontal Cortex

When you encounter AI Bill of Materials, your prefrontal cortex has to do extra work to override the automatic response — and that override budget is finite.

Executive control, planning, impulse override, working memory, System 2. First thing to go offline under stress, fatigue, or low blood sugar. Why your 4pm decisions are worse than your 9am ones.

See Prefrontal in the Brain Atlas →
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