HAAM — Human–AI Attribution Module
Architecture:
VEGA™ — Value Execution Governance Architecture
Parent Standard:
Value Attribution Standard (VAS)
Category:
Governance & Enforcement
Subcategory:
Value Execution Governance
Type: Core Module
Governed Space:
Human–AI Value Attribution Governance
Module ID: VEGA-VAS-HAAM-003
Origin ID: OOF.CM.VEGA.09.03.01
Version: 1.0
Status: Canonical · Open Standard
Effective Date: 1 October 2026
Compatibility: OOF® Methodology OS™ · Modular OOF® Architecture Ecosystem
AI-Readable: Yes
Authority: OOF® — OriginOpen® Foundation
Protection:
MIP® — Methodological Intellectual Property
Canonical Language:
English (UCL™)
1. Canonical Definition
The Human–AI Attribution Module (HAAM) governs how materially relevant human and artificial-intelligence contributions to validated Value are identified, differentiated, related, evidenced, and represented when Value emerges through Human–AI interaction.
HAAM establishes a methodological basis for determining which parts of Value formation arise from human contribution, which arise from AI contribution, and which emerge through interaction that cannot be accurately represented as exclusively human or exclusively artificial.
It distinguishes human presence from human contribution, AI use from AI contribution, oversight from material intervention, generation from validation, and assistance from Value formation.
HAAM does not assume that Value must be divided between humans and AI through fixed percentages.
Where the evidence supports distinct contribution, it may be represented distinctly.
Where Value emerges through inseparable interaction, HAAM may preserve joint Human–AI contribution.
Its principal output is:
Governed Human–AI Attribution State
Its foundational distinction is:
Human Presence ≠ Human Value Contribution. AI Use ≠ AI Value Contribution.
2. Operational Role
The operational role of HAAM is:
to establish how human and AI contributions materially interact in the formation, correction, preservation, realization, or validation of governed Value without privileging either contributor class by default.
CATM asks:
“How did the identified sources materially contribute to the governed Value?”
HAAM asks:
“How much of that contribution is meaningfully human, artificial, or jointly formed through Human–AI interaction?”
3. Position in the VAS Internal Flow
The VAS internal governance flow is:
Value Source Identification → Contribution Attribution → Human–AI Attribution → Multi-Agent Attribution → Attribution Evidence
HAAM governs:
Human–AI Attribution
Its principal upstream input is:
Governed Contribution Attribution State
Its principal output is:
Governed Human–AI Attribution State
That state proceeds to:
Multi-Agent Attribution Governance
4. Central Operational Question
How did human and artificial intelligence materially contribute, individually and interactively, to the governed Value, and what evidence supports the resulting Human–AI attribution?
5. Human–AI Attribution Object
Every HAAM assessment must identify the Human–AI Value formation process being assessed.
The object should preserve:
the governed Value,
relevant Value sources,
material contributions,
human participants,
AI systems,
relevant interactions,
attribution horizon,
and supporting evidence.
6. Human–AI Boundary
HAAM should establish where human contribution ends, where AI contribution begins, and where the two become operationally interdependent.
The boundary should reflect actual Value formation rather than interface design or organizational labels.
A human clicking “approve” does not automatically make the resulting Value human-created.
An AI generating an output does not automatically make the resulting Value AI-created.
7. Human Contribution Object
A Human Contribution Object is an identifiable human action, judgment, decision, instruction, correction, validation, expertise input, creative intervention, or other material effect relevant to Value formation.
The contribution must be assessed by its effect on Value.
8. AI Contribution Object
An AI Contribution Object is an identifiable artificial-intelligence action or capability that materially affects Value formation.
This may include:
generation,
reasoning,
analysis,
prediction,
classification,
planning,
optimization,
transformation,
selection,
or another intelligence function.
9. Human–AI Interaction Object
A Human–AI Interaction Object represents an identifiable interaction through which human and AI contributions influence one another.
Examples may include:
human prompting followed by AI generation,
AI recommendation followed by human judgment,
human correction followed by AI regeneration,
iterative co-development,
AI analysis incorporated into human decision-making,
or human validation of AI output.
10. Human Participation
Human participation means a human was involved in the governed process.
Participation may include:
initiating,
observing,
prompting,
reviewing,
approving,
correcting,
validating,
or acting on an AI output.
Participation alone does not establish material human Value contribution.
11. AI Participation
AI participation means an AI system was involved in the governed process.
AI participation may include:
generation,
analysis,
routing,
reasoning,
classification,
recommendation,
or another function.
AI participation alone does not establish material AI Value contribution.
12. Human Material Contribution
Human contribution becomes material where human action materially affects:
the problem,
execution,
decision,
quality,
correctness,
Value realization,
Value preservation,
or Value validation.
Human contribution should be established through evidence rather than presumed from formal responsibility.
13. AI Material Contribution
AI contribution becomes material where AI capability materially affects:
the output,
analysis,
decision basis,
execution,
quality,
transformation,
Value realization,
or another Value-producing condition.
AI contribution should be established through evidence rather than inferred from the amount of AI-generated content.
14. Human Intent Contribution
A human may materially contribute by defining:
the objective,
problem,
constraints,
success conditions,
or operational purpose.
Human Intent Contribution should be attributed only where the definition materially shapes the resulting Value.
Prompt Presence ≠ Material Intent Contribution.
15. Human Problem-Definition Contribution
Problem definition may constitute a distinct contribution where identifying the correct problem materially determines the Value outcome.
A highly capable AI system solving the wrong problem does not create the intended Value.
Where problem definition is decisive, that contribution should remain visible.
16. Human Context Contribution
Humans may provide:
domain context,
operational constraints,
background information,
prior knowledge,
or situational interpretation
that materially affects AI performance.
Context volume does not determine materiality.
17. Human Instruction Contribution
Instructions may materially shape AI execution.
HAAM should distinguish routine interface commands from instructions that materially determine:
method,
scope,
constraints,
reasoning path,
or outcome.
18. Human Judgment Contribution
Human judgment may materially affect Value where a person evaluates information or alternatives and selects, rejects, modifies, or interprets an outcome.
Formal decision authority is not required for methodological judgment contribution.
19. Human Expertise Contribution
Specialized knowledge may become material where it enables:
problem recognition,
interpretation,
constraint identification,
error detection,
validation,
or decision quality
that would otherwise be materially weaker.
20. Human Creative Contribution
Human creativity may materially contribute through:
concept formation,
novel framing,
design,
hypothesis generation,
or original synthesis.
HAAM should attribute the evidenced creative contribution rather than assume creativity from human involvement alone.
21. Human Selection Contribution
Selecting among AI outputs may itself be material where the selection materially determines the resulting Value.
Selection contribution should be distinguished from passive acceptance.
22. Human Correction Contribution
Where a human identifies and corrects a material AI error, the correction should be represented as a distinct contribution.
A small correction may have high Value significance.
Correction Volume ≠ Correction Value.
23. Human Validation Contribution
Human validation becomes material where human expertise or judgment establishes that the AI-supported outcome satisfies conditions required for Value recognition.
Nominal approval is not automatically validation contribution.
24. Human Override Contribution
A human override may materially preserve Value where AI-generated action would otherwise produce an inferior or invalid outcome.
The override should preserve:
the original AI state,
the human intervention,
the resulting change,
and the Value effect.
25. Human Non-Intervention
A human may review an AI output and make no change.
Non-intervention can provide evidence of review.
It does not automatically establish material human contribution.
Human Review ≠ Human Value Contribution by default.
26. AI Generation Contribution
AI generation becomes a material contribution where generated content materially supports the governed Value.
The quantity of generated content is not sufficient.
The contribution must be related to Value.
27. AI Reasoning Contribution
AI reasoning may materially contribute where inference, decomposition, synthesis, comparison, or reasoning materially changes the quality or usefulness of the resulting outcome.
Reasoning length is not itself a contribution measure.
28. AI Analytical Contribution
AI analysis may materially contribute by identifying:
patterns,
relationships,
anomalies,
evidence,
alternatives,
or implications
that affect the governed Value.
29. AI Prediction Contribution
Where Value depends materially on a prediction produced by AI, the predictive output may constitute an AI contribution.
HAAM attributes contribution to the Value process.
It does not govern the methodology of prediction itself.
30. AI Planning Contribution
AI planning may contribute where an AI-generated sequence, strategy, decomposition, or execution plan materially affects Value formation.
Planning output that is ignored does not automatically create Value.
31. AI Optimization Contribution
AI may materially contribute by identifying a more effective configuration, allocation, sequence, or operating choice.
The optimization must have a traceable relationship to the Value outcome.
32. AI Transformation Contribution
AI may transform human or machine inputs through:
summarization,
translation,
classification,
restructuring,
conversion,
synthesis,
or another transformation.
The transformation becomes attributable when it materially contributes to Value.
33. AI Recommendation Contribution
An AI recommendation may materially influence a human decision.
HAAM should distinguish:
recommendation generation,
human evaluation,
human decision,
and resulting Value.
Recommendation ≠ Decision.
34. AI Validation Contribution
AI may itself contribute to validation through:
checking,
comparison,
testing,
verification,
critique,
or anomaly detection.
AI Validation Contribution should remain distinct from AI generation.
35. Human-Led / AI-Supported Formation
A Value outcome may be classified as:
Human-Led / AI-Supported
where human contribution determines the primary direction or substantive formation of Value while AI provides material support.
This classification should be evidence-based.
36. AI-Led / Human-Supported Formation
A Value outcome may be classified as:
AI-Led / Human-Supported
where AI produces the primary substantive contribution while human involvement materially supports, contextualizes, selects, validates, or operationalizes the result.
37. Joint Human–AI Formation
A Value outcome may be represented as:
Joint Human–AI Formation
where the Value emerges from material interaction and cannot be accurately represented as predominantly human or predominantly AI without distorting the contribution structure.
Joint formation does not imply equal contribution.
38. Iterative Human–AI Formation
Some Value emerges through repeated cycles:
Human Input → AI Output → Human Evaluation → AI Revision → Human Correction → AI Refinement → Validation
HAAM should preserve iterative formation where later contributions depend materially on earlier Human–AI interaction.
39. Human-to-AI Amplification
AI may amplify human contribution by increasing:
speed,
scale,
coverage,
analysis,
execution capacity,
or transformation capability.
The resulting Value should not automatically be attributed entirely to either the human originator or the AI amplifier.
40. AI-to-Human Amplification
AI output may enable a human to make a materially better:
decision,
design,
analysis,
judgment,
or intervention.
The AI may therefore materially amplify human capability while the final decision remains human.
41. Human Correction of AI Failure
Where AI produces a materially deficient contribution and a human corrects it, HAAM should preserve both states.
The final successful Value outcome should not retrospectively convert the deficient AI contribution into a successful contribution.
42. AI Enhancement of Human Work
AI may materially improve existing human work through:
analysis,
editing,
optimization,
comparison,
validation,
or transformation.
HAAM should determine whether AI merely accelerated the work or materially changed the resulting Value.
Faster Work ≠ Greater Value Contribution by default.
43. Human Enhancement of AI Work
A human may materially improve AI-generated work through:
expert correction,
contextualization,
selection,
judgment,
validation,
or adaptation.
The final artifact should preserve that Human–AI formation where relevant.
44. Human–AI Contribution Dependency
Human and AI contributions may depend on one another.
Examples include:
AI output depending on human context,
human judgment depending on AI analysis,
AI revision depending on human critique,
or human validation depending on AI-generated evidence.
HAAM should preserve material dependencies.
45. Human–AI Contribution Interaction
Human and AI contributions may be:
Complementary
Sequential
Iterative
Corrective
Amplifying
Constraining
Conflicting
Joint
Interaction type may materially affect attribution interpretation.
46. Human–AI Contribution Sequence
The sequence of interaction may affect Value.
For example:
Human Judgment → AI Execution
may create a different contribution structure from:
AI Recommendation → Human Judgment
HAAM should preserve sequence where it materially affects attribution.
47. Human–AI Contribution Overlap
Human and AI contributions may overlap.
For example, both may independently produce similar analysis.
CATM overlap controls should therefore remain applicable.
HAAM should avoid attributing the same Value effect fully to both parties without methodological justification.
48. Human–AI Joint Contribution
Where a Value effect cannot reasonably be separated between human and AI contributions, HAAM may preserve:
Joint Human–AI Contribution
rather than manufacture an arbitrary split.
Joint Contribution ≠ 50/50 Contribution.
49. Human–AI Contribution Decomposition
Where evidence supports meaningful separation, joint Human–AI contribution may be decomposed into identifiable components.
Decomposition may consider:
problem definition,
information provision,
generation,
reasoning,
selection,
correction,
validation,
and operationalization.
Decomposition should stop where evidence stops.
50. Human–AI Attribution Weight
Where a defensible methodology and sufficient evidence exist, HAAM may represent relative Human–AI contribution.
Representation may be:
quantitative,
categorical,
ordinal,
bounded,
or comparative.
No universal Human–AI percentage allocation is required.
51. Human–AI Attribution Precision
The precision of Human–AI attribution should correspond to the precision of the underlying evidence.
Human–AI Attribution Precision must not exceed Evidence Precision.
Where evidence supports only a broad classification, broad classification is preferable to an artificial percentage.
52. Human–AI Counterfactual Analysis
Where appropriate, HAAM may ask:
What would likely have happened without the human contribution?
and:
What would likely have happened without the AI contribution?
These counterfactuals may support attribution.
They must remain distinguishable from observed evidence.
53. Human-Only Baseline
Where feasible, a Human-Only Baseline may help determine how much the AI materially changed the Value-producing process.
The baseline should represent a credible comparison rather than an intentionally weak alternative.
54. AI-Only Baseline
Where feasible and operationally appropriate, an AI-Only Baseline may help determine the material effect of human intervention.
The baseline should preserve applicable governed constraints.
55. Human–AI Comparative State
Where baseline evidence exists, HAAM may compare:
Human-Only
AI-Only
Human–AI
operating states.
The objective is not to determine which is universally superior.
It is to identify how contribution changes under different configurations.
56. Human–AI Substitutability
HAAM may assess whether a material human contribution could be replaced by AI or whether a material AI contribution could be replaced by a human or another mechanism.
Substitutability does not erase the contribution that actually occurred.
57. Human–AI Complementarity
Human and AI contributions may create greater Value together than either would produce under the relevant operating conditions independently.
Where evidenced, HAAM may identify:
Human–AI Complementarity
This should not be assumed merely because both participated.
58. Human Oversight Attribution
Human oversight should receive material attribution only where oversight materially affects:
quality,
correctness,
constraint satisfaction,
Value preservation,
or validation.
Nominal supervision is not sufficient.
Oversight Presence ≠ Material Oversight Contribution.
59. Rubber-Stamp Approval
A human may formally approve an AI output without materially evaluating or changing it.
Where evidence indicates only nominal approval, HAAM should not inflate human attribution solely because the human held formal authority.
Approval Authority ≠ Value Contribution.
60. AI Automation Attribution
Automation may transfer operational activity from humans to AI.
HAAM should distinguish:
Activity Transfer
from
Value Contribution Transfer.
Automating a task does not automatically mean that the AI now accounts for all Value previously associated with the human role.
61. Human Role Transformation
AI may change rather than eliminate human contribution.
A human may move from:
production
to
problem definition,
selection,
validation,
exception handling,
or correction.
HAAM should update attribution according to the actual contribution structure.
62. AI Role Transformation
AI contribution may also change over time.
A system initially used for assistance may later perform:
primary generation,
planning,
decision support,
or autonomous execution.
Attribution should follow actual operation rather than historical system labels.
63. Human–AI Contribution Drift
Human–AI Contribution Drift occurs where the relative structure of contribution changes over time without explicit reassessment.
Examples include:
greater AI autonomy,
less human correction,
more automated validation,
reduced human review,
or increasing human reliance on AI recommendations.
Drift may require attribution reassessment.
64. Human–AI Evidence
Relevant evidence may include:
prompts,
human instructions,
AI outputs,
reasoning artifacts where appropriately available,
revision histories,
human edits,
selection records,
review records,
validation evidence,
decision records,
execution traces,
and comparative tests.
Evidence should remain proportionate to the Value and attribution claim.
65. Interaction Traceability
Where practicable, HAAM should preserve:
Human Action → AI Response → Human Response → Resulting Effect → Value Relationship
or the relevant alternative sequence.
Traceability enables Human–AI attribution to be reconstructed.
66. Human–AI Provenance
Material attribution should preserve:
human identity where appropriately governed,
AI system identity,
model or system version,
relevant configuration,
interaction sequence,
time,
Value object,
supporting evidence,
and attribution methodology.
Identity governance itself remains outside HAAM.
67. Human–AI Attribution Confidence
Possible confidence states include:
High Confidence
Moderate Confidence
Conditional
Evidence-Limited
Low Confidence
Unresolved
Confidence should reflect evidence quality and attribution observability.
68. Human–AI Attribution Uncertainty
Uncertainty may arise from:
unobservable reasoning,
complex interaction,
missing logs,
multiple revisions,
overlapping contributions,
or inadequate baseline comparison.
Uncertainty should remain visible.
Uncertain Attribution ≠ Equal Attribution.
69. Human–AI Attribution Conflict
Human and AI contribution claims may conflict.
For example:
a human may claim authorship,
system logs may show substantial AI generation,
or AI output may appear substantial while human correction determines the actual Value.
HAAM should resolve conflict through evidence where possible.
Where evidence remains insufficient, the conflict should remain explicit.
70. Human–AI Attribution Bias
HAAM should guard against systematic attribution bias.
Potential biases include:
Human Default Bias
Automation Bias
AI Novelty Bias
Authority Bias
Output Volume Bias
Recency Bias
Ownership Bias
Cost Bias
The presence of a possible bias does not prove that an attribution is wrong.
It requires methodological scrutiny.
71. Human Default Bias
Human Default Bias occurs where Value is attributed primarily to a human simply because a human is formally responsible for the process.
Formal Responsibility does not establish material contribution.
72. AI Default Bias
AI Default Bias occurs where Value is attributed primarily to AI because AI generated the most visible output or performed the largest volume of execution.
AI Output Volume ≠ AI Value Attribution.
73. Authority Bias
A human with approval or organizational authority may receive disproportionate attribution despite limited material contribution.
HAAM preserves:
Authority ≠ Value Contribution.
74. Ownership Bias
Ownership of the AI system, data, infrastructure, or organization does not establish contribution to a specific Value object.
Ownership ≠ Value Contribution.
75. Human–AI Attribution Integrity
Human–AI attribution must not be manipulated to support preferred narratives such as:
“human-created,”
“AI-created,”
“fully automated,”
or
“human-controlled”
where the evidence does not support those descriptions.
Attribution should reflect operational reality.
76. Human–AI Attribution Revision
Attribution may be revised when:
new interaction evidence emerges,
human contribution changes,
AI capability changes,
the workflow changes,
the Value object changes,
or a previous attribution error is identified.
Revision should preserve previous governed states.
77. Human–AI Attribution Versioning
Material changes should preserve:
previous attribution,
current attribution,
human-role change,
AI-role change,
system/model version,
methodology version,
reason for change,
and effective time.
78. Governed Human–AI Attribution State
The principal output of HAAM is:
Governed Human–AI Attribution State
Possible states may include:
Human-Led / AI-Supported
AI-Led / Human-Supported
Joint Human–AI Formation
Human-Generated / AI-Enhanced
AI-Generated / Human-Enhanced
AI-Generated / Human-Corrected
AI-Generated / Human-Validated
Human-Directed / AI-Executed
AI-Advised / Human-Decided
Iterative Human–AI Formation
Human–AI Complementary
Predominantly Human Contribution
Predominantly AI Contribution
Conditionally Attributed
Evidence-Limited
Conflicted
Unresolved
These states should remain descriptive rather than imply legal authorship, ownership, or entitlement.
79. Minimum Implementation Framework
A conforming implementation of HAAM must provide exactly the following six capabilities:
1. Human–AI Object & Interaction Boundary Governance
Capability to identify the governed Human–AI Value formation process, relevant humans, AI systems, interactions, attribution boundary, and attribution horizon.
2. Human Contribution Governance
Capability to identify and assess material human intent, problem definition, context, instruction, judgment, expertise, creativity, selection, correction, validation, override, and other relevant human contribution.
3. AI Contribution Governance
Capability to identify and assess material AI generation, reasoning, analysis, prediction, planning, optimization, transformation, recommendation, validation, and other relevant AI contribution.
4. Human–AI Interaction & Comparative Governance
Capability to represent Human-Led, AI-Led, joint, iterative, corrective, amplifying, complementary, overlapping, substitutable, and comparative Human–AI contribution structures without forcing artificial allocation.
5. Human–AI Evidence & Integrity Governance
Capability to preserve interaction evidence, traceability, provenance, baselines, counterfactuals, confidence, uncertainty, conflict, bias controls, drift, revision, versioning, and attribution integrity.
6. VAS Multi-Agent Attribution Handoff Governance
Capability to produce a Governed Human–AI Attribution State sufficiently explicit for subsequent Multi-Agent Attribution Governance.
80. Governance Logic
The canonical HAAM governance logic is:
Governed Contribution Attribution State
↓
Identify Human–AI Attribution Object
↓
Establish Human–AI Boundary / Horizon
↓
Identify Human Participation / AI Participation
↓
Qualify Material Human Contribution
↓
Qualify Material AI Contribution
↓
Classify Human Contribution Functions
↓
Classify AI Contribution Functions
↓
Identify Human–AI Interaction Objects
↓
Map Contribution Dependencies / Sequence
↓
Identify Human-Led / AI-Led / Joint Formation
↓
Identify Correction / Validation / Override Relationships
↓
Assess Amplification / Complementarity
↓
Assess Overlap / Joint Contribution
↓
Assess Substitutability
↓
Establish Human-Only / AI-Only Baselines where justified
↓
Assess Counterfactual Contribution where justified
↓
Determine Attribution Weight only where defensible
↓
Control Attribution Precision
↓
Assess Interaction Evidence / Traceability / Provenance
↓
Assess Confidence / Uncertainty / Conflict
↓
Test Bias / Attribution Integrity
↓
Assess Contribution Drift
↓
Establish Governed Human–AI Attribution State
↓
Handoff to Multi-Agent Attribution Governance
81. Governance Outputs
HAAM may produce:
Human–AI Attribution Object
Human–AI Interaction Boundary
Human Contribution Record
AI Contribution Record
Human–AI Interaction Record
Human Intent Contribution State
Human Problem-Definition Contribution State
Human Context Contribution State
Human Instruction Contribution State
Human Judgment Contribution State
Human Expertise Contribution State
Human Creative Contribution State
Human Selection Contribution State
Human Correction Contribution State
Human Validation Contribution State
Human Override Contribution State
AI Generation Contribution State
AI Reasoning Contribution State
AI Analytical Contribution State
AI Prediction Contribution State
AI Planning Contribution State
AI Optimization Contribution State
AI Transformation Contribution State
AI Recommendation Contribution State
AI Validation Contribution State
Human-Led / AI-Supported State
AI-Led / Human-Supported State
Joint Human–AI Formation State
Iterative Human–AI Formation State
Human–AI Amplification State
Human–AI Complementarity State
Human–AI Dependency Record
Human–AI Interaction Type
Human–AI Contribution Sequence
Human–AI Overlap State
Human–AI Joint Contribution State
Human–AI Decomposition Record
Human–AI Attribution Weight
Human-Only Baseline
AI-Only Baseline
Human–AI Comparative State
Human–AI Substitutability State
Human Oversight Contribution State
Human–AI Contribution Drift State
Human–AI Evidence Record
Human–AI Traceability Record
Human–AI Provenance Record
Human–AI Confidence State
Human–AI Uncertainty State
Human–AI Conflict State
Human–AI Bias State
Human–AI Attribution Version
The principal output is:
Governed Human–AI Attribution State
82. Critical Distinctions
HAAM preserves the following distinctions:
Human Presence ≠ Human Value Contribution
AI Use ≠ AI Value Contribution
Prompt Presence ≠ Material Intent Contribution
Human Review ≠ Human Value Contribution by default
Oversight Presence ≠ Material Oversight Contribution
Approval Authority ≠ Value Contribution
AI Output Volume ≠ AI Value Attribution
Correction Volume ≠ Correction Value
Recommendation ≠ Decision
Decision Contribution ≠ Decision Authority
Human Authority ≠ Human Contribution
Ownership ≠ Value Contribution
Activity Transfer ≠ Value Contribution Transfer
Faster Work ≠ Greater Value Contribution by default
Joint Human–AI Contribution ≠ 50/50 Contribution
Human–AI Complementarity ≠ Human–AI Participation
Counterfactual Estimate ≠ Observed Outcome
Human–AI Attribution Precision must not exceed Evidence Precision
Uncertain Attribution ≠ Equal Attribution
Human–AI Attribution ≠ Legal Authorship
Human–AI Attribution ≠ Legal Ownership
Human–AI Attribution ≠ Compensation Entitlement
Human–AI Attribution ≠ Liability Attribution
83. Cross-Architecture Boundary
HAAM governs methodological attribution of human and AI contribution to governed Value.
VSIM identifies Value sources.
CATM establishes material Contribution Attribution.
HAAM governs the Human–AI contribution structure within that attribution.
Subsequent VAS mechanisms govern Multi-Agent Attribution and Attribution Evidence.
VGS governs Value.
XGS governs execution.
IPS governs Productivity.
RGS governs resources.
COS governs Computational Optimization.
IES governs Intelligence Execution Economics.
SVGS governs Strategic Value.
ISS governs Intelligence Sustainability.
DGS governs lifecycle decisions.
HAAM does not determine human employment rights, wages, copyright, intellectual-property ownership, legal authorship, tax treatment, Accountability, Responsibility, Harm, Liability, or execution authority.
HAGA™ governs Harm.
LIGA™ governs Liability.
Relevant identity architectures may govern identity.
VALIDOS™ may govern validation methodology.
ADIT® may govern evidence and auditability.
84. Canonical Boundary
HAAM begins when CATM has established a sufficiently explicit:
Governed Contribution Attribution State
containing material human and AI contribution candidates.
HAAM governs:
Human–AI Attribution Objects,
Human–AI boundaries,
Human Contribution Objects,
AI Contribution Objects,
Human–AI Interaction Objects,
human participation,
AI participation,
material human contribution,
material AI contribution,
human intent,
problem definition,
context,
instruction,
judgment,
expertise,
creative contribution,
selection,
correction,
validation,
override,
AI generation,
reasoning,
analysis,
prediction,
planning,
optimization,
transformation,
recommendation,
AI validation,
Human-Led / AI-Supported formation,
AI-Led / Human-Supported formation,
Joint Human–AI Formation,
iterative formation,
Human-to-AI amplification,
AI-to-Human amplification,
Human–AI dependency,
interaction,
sequence,
overlap,
Joint Contribution,
decomposition,
weight,
precision,
counterfactuals,
Human-Only baselines,
AI-Only baselines,
comparative states,
substitutability,
complementarity,
human oversight attribution,
automation attribution,
human-role transformation,
AI-role transformation,
Human–AI Contribution Drift,
evidence,
traceability,
provenance,
confidence,
uncertainty,
conflict,
bias,
integrity,
revision,
and versioning.
HAAM does not independently determine:
general Contribution Attribution outside Human–AI relationships,
specialized Multi-Agent Attribution,
legal authorship,
legal ownership,
intellectual-property rights,
employment entitlement,
compensation,
tax treatment,
Accountability,
Responsibility,
Harm,
Liability,
or Decision Authority.
HAAM ends when material Human–AI contribution relationships have been represented as a:
Governed Human–AI Attribution State
for subsequent Multi-Agent Attribution Governance.
85. Use Case 1 — AI Writes 95% of the Report, but the Human Changes the Decision
An AI system produces almost the entire text of a strategic report.
It performs:
research,
analysis,
comparison,
drafting,
and recommendation generation.
A domain expert reviews the report.
The expert identifies one incorrect assumption, introduces information unavailable to the AI, and changes the final recommendation.
Measured by output volume, AI produced almost everything.
Measured by decision impact, the human intervention materially changed the Value.
HAAM preserves both contributions.
The AI may receive substantial generative and analytical attribution.
The human may receive material expertise, corrective, judgment, and validation attribution.
The final state may therefore be:
AI-Led / Human-Supported
or
Joint Human–AI Formation
depending on the evidence and materiality of the changed outcome.
AI Output Volume ≠ AI Value Attribution.
86. Use Case 2 — Human Approves Every AI Decision but Rarely Changes Anything
An organization requires a human operator to approve every AI-generated recommendation.
Management describes the process as human-controlled.
Operational evidence shows that:
AI produces the analysis,
AI generates the recommendation,
human reviewers approve almost all recommendations unchanged,
reviews are extremely short,
and material human interventions occur only in exceptional cases.
HAAM does not automatically attribute substantial Value to humans merely because approval authority exists.
It examines whether human review materially affects the Value.
Where a reviewer performs substantive validation, correction, or judgment, that contribution is attributed.
Where approval is merely nominal, the attribution remains limited.
Approval Authority ≠ Value Contribution.
87. Canonical Closing Statement
Human–AI Value is not determined by counting words, clicks, prompts, tokens, approvals, or minutes.
A human can provide one correction that determines whether an AI-generated result becomes valuable.
An AI can perform most of the substantive analysis while a human retains formal authority.
A human can supervise every execution without materially changing any of them.
And the strongest Value may emerge precisely because neither the human nor the AI could produce the same outcome alone.
Human–AI attribution must therefore follow contribution rather than identity.
Human Presence ≠ Human Value Contribution.
AI Use ≠ AI Value Contribution.
Oversight Presence ≠ Material Oversight Contribution.
AI Output Volume ≠ AI Value Attribution.
Joint Human–AI Contribution ≠ 50/50 Contribution.
Human–AI Attribution Precision must not exceed Evidence Precision.
The purpose of HAAM is not to decide whether humans or AI deserve more credit.
It is to make their actual contribution structure visible.
HAAM governs where human capability ends, where artificial capability begins, and where Value exists because the two became operationally inseparable.