AEM — Attribution Evidence 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:

Value Attribution Evidence Governance

Module ID: VEGA-VAS-AEM-005

Origin ID: OOF.CM.VEGA.09.05.01

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 Attribution Evidence Module (AEM) governs whether Value Attribution is supported by sufficient, relevant, traceable, provenance-preserved, reproducible, challengeable, and methodologically appropriate evidence.

AEM establishes the evidence conditions required to support attribution claims concerning Value sources, material contributions, Human–AI contribution, Multi-Agent contribution, and the resulting Governed Value Attribution State.

It governs the relationship between:

Attribution Claim → Supporting Evidence → Evidence Quality → Provenance → Traceability → Confidence → Challenge → Governed Attribution Evidence State

AEM does not determine contribution merely because evidence exists.

It determines whether the evidence supporting an attribution is sufficient for the attribution claim being made.

Its principal module output is:

Governed Attribution Evidence State

As the final VAS Core Module, AEM also supports consolidation into:

Governed Value Attribution State

Its foundational distinction is:

Evidence Availability ≠ Attribution Evidence Sufficiency.

2. Operational Role

The operational role of AEM is:

to determine whether the evidence supporting Value Attribution is sufficiently relevant, complete, reliable, traceable, provenance-preserved, reproducible, and challengeable to justify the attribution state being asserted.

MAAM establishes Multi-Agent Attribution.

AEM asks:

“What evidence supports the attribution, how strong is that evidence, and does it justify the precision and confidence of the attribution claim?”

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

AEM governs:

Attribution Evidence

Its principal upstream states include:

Governed Value Source State

Governed Contribution Attribution State

Governed Human–AI Attribution State

Governed Multi-Agent Attribution State

Its principal module output is:

Governed Attribution Evidence State

That output supports consolidation into:

Governed Value Attribution State

The consolidated VAS state then proceeds to:

Decision Governance

4. Central Operational Question

Is the evidence supporting the governed attribution sufficiently relevant, reliable, complete, traceable, provenance-preserved, reproducible, and challengeable to justify the attribution claim and its stated level of precision and confidence?

5. Attribution Evidence Object

Every AEM assessment should define the Attribution Evidence Object.

The object should identify:

the Value object,

the attribution claim,

the contributor or contribution concerned,

the attribution methodology,

the evidence supporting the claim,

and the applicable attribution state.

Evidence should be evaluated against a defined claim rather than in isolation.

6. Attribution Claim

An Attribution Claim is a methodological assertion concerning:

a Value source,

a contribution,

a contributor,

a contribution relationship,

a Human–AI attribution,

a Multi-Agent attribution,

a contribution weight,

or another material attribution conclusion.

AEM governs whether the evidence justifies that claim.

7. Claim-to-Evidence Relationship

Every material Attribution Claim should have an identifiable relationship to supporting evidence.

The canonical relationship is:

Attribution Claim ↔ Supporting Evidence

A collection of records is not sufficient merely because it exists.

The records must support the relevant claim.

8. Evidence Scope

Evidence scope should correspond to the scope of the attribution claim.

A narrow evidence base should not support a broader attribution claim than it can reasonably establish.

Evidence Scope must support Claim Scope.

9. Evidence Relevance

Evidence is relevant where it materially informs whether the attribution claim is supported.

Evidence volume does not establish relevance.

A small amount of directly relevant evidence may be stronger than a large quantity of unrelated records.

10. Evidence Materiality

AEM should distinguish material evidence from peripheral evidence.

Material evidence is evidence capable of materially affecting the attribution conclusion.

Evidence that cannot reasonably change or support the attribution state should not be given artificial importance.

11. Direct Attribution Evidence

Direct Attribution Evidence has an immediate evidentiary relationship to the contribution claim.

Examples may include:

recorded human edits,

agent execution records,

model outputs,

tool calls,

validation events,

or documented contribution events.

Direct evidence does not automatically establish sufficient attribution.

12. Indirect Attribution Evidence

Indirect Attribution Evidence supports a claim through an intermediate relationship.

Examples may include:

system records,

workflow dependencies,

historical comparisons,

or supporting operational evidence.

Indirect Evidence ≠ Weak Evidence by default.

Its strength depends on the relationship to the attribution claim.

13. Observed Evidence

Observed Evidence records what actually occurred in the governed process.

It may include:

actions,

outputs,

interactions,

changes,

decisions,

corrections,

validation events,

and execution states.

Observed evidence should remain distinguishable from inferred or counterfactual evidence.

14. Inferred Evidence

Inferred Evidence is produced through interpretation of observed facts.

Inference may be necessary where direct observation is unavailable.

The inference method and assumptions should remain explicit.

Inference ≠ Observation.

15. Counterfactual Evidence

Counterfactual analysis may support attribution by estimating what would likely have occurred if a contribution had been absent or different.

Counterfactual evidence should preserve:

baseline,

assumptions,

method,

uncertainty,

and comparison conditions.

Counterfactual Estimate ≠ Observed Outcome.

16. Comparative Evidence

Comparative Evidence may evaluate differences between:

configurations,

contributors,

models,

agents,

workflows,

Human-Only operation,

AI-Only operation,

Human–AI operation,

or alternative mechanisms.

Comparisons should use sufficiently comparable conditions.

17. Experimental Evidence

Where feasible and proportionate, controlled experimentation may provide stronger evidence of contribution.

Experiments may include:

component removal,

agent removal,

model replacement,

workflow variation,

A/B comparison,

or controlled execution.

Experimental evidence should preserve the conditions under which the comparison was conducted.

18. Operational Evidence

Operational Evidence comes from actual system operation.

It may provide strong evidence of real-world contribution where execution conditions are sufficiently observable.

Operational success alone does not establish which contributor created the Value.

19. Human Attribution Evidence

Evidence supporting human contribution may include:

instructions,

problem definitions,

edits,

decisions,

reviews,

corrections,

validation,

override actions,

domain inputs,

or documented judgments.

Human identity alone is not contribution evidence.

20. AI Attribution Evidence

Evidence supporting AI contribution may include:

AI outputs,

execution traces,

model responses,

analysis,

reasoning artifacts where appropriately available,

recommendations,

transformations,

or other documented AI actions.

AI invocation alone is not sufficient contribution evidence.

21. Multi-Agent Attribution Evidence

Multi-Agent evidence may include:

agent identity,

task assignments,

delegation,

messages,

outputs,

routing,

tool calls,

validation events,

corrections,

conflicts,

handoffs,

and execution sequences.

The evidence should enable reconstruction of material agent relationships where required.

22. Tool Attribution Evidence

Tool evidence may include:

tool invocation,

input,

output,

execution result,

time,

caller,

and subsequent Value effect.

Tool invocation alone does not establish tool Value contribution.

23. Data Attribution Evidence

Data contribution evidence may include:

data source,

version,

provenance,

retrieval event,

content used,

transformation,

and relationship to the resulting Value.

A dataset being available does not establish that it materially contributed.

24. Infrastructure Attribution Evidence

Infrastructure evidence may include:

hardware use,

compute allocation,

storage,

networking,

runtime environment,

availability,

or another infrastructure condition.

Resource consumption alone does not establish Value contribution.

25. Methodology Attribution Evidence

Evidence supporting methodology contribution may include:

methodology version,

process implementation,

decision logic,

measurement logic,

validation logic,

or operational outcomes linked to its use.

The existence of a methodology does not establish that it materially affected the governed Value.

26. Evidence Identity

Material evidence should be sufficiently identifiable.

Where applicable, identity may include:

record identifier,

source,

system,

contributor,

timestamp,

version,

or another persistent reference.

Evidence identity supports traceability.

27. Evidence Origin

AEM should preserve where evidence originated.

Evidence Origin may identify:

human source,

system source,

agent source,

model source,

tool source,

data source,

infrastructure source,

or external source.

Origin does not determine reliability by itself.

28. Evidence Provenance

Evidence Provenance should preserve the relevant history of evidence from origin to attribution use.

Where applicable, provenance may include:

creation,

collection,

transformation,

transfer,

storage,

versioning,

and use.

Evidence without sufficient provenance may support less attribution certainty.

29. Evidence Temporal Integrity

Evidence should preserve when the relevant event occurred.

Temporal integrity is important where:

multiple versions exist,

contribution changes over time,

events occur sequentially,

or later changes could alter interpretation.

30. Evidence Version Integrity

Where evidence can change, AEM should preserve the version used for attribution.

A later version should not silently replace the evidentiary basis of an earlier attribution state.

31. Evidence Integrity

Evidence should be sufficiently protected against:

unauthorized alteration,

selective modification,

loss,

corruption,

or undocumented replacement.

AEM governs whether evidence integrity is sufficient for attribution purposes.

It does not replace broader cybersecurity or records-management governance.

32. Evidence Completeness

Evidence Completeness evaluates whether material parts of the attribution claim are sufficiently supported.

Completeness does not require every possible record.

It requires enough evidence to support the material attribution conclusion.

33. Evidence Coverage

Coverage evaluates which portions of the contribution structure are evidenced.

AEM may identify:

fully evidenced,

partially evidenced,

unevidenced,

or unresolved contribution relationships.

High evidence volume can still have poor coverage.

34. Evidence Specificity

Evidence should be sufficiently specific to the relevant:

Value object,

contributor,

contribution,

event,

or attribution claim.

Generic evidence should not support specific attribution beyond what it can establish.

35. Evidence Consistency

Evidence Consistency evaluates whether relevant evidence sources materially agree.

Consistency strengthens confidence.

Inconsistency does not automatically invalidate attribution but requires resolution or explicit preservation.

36. Evidence Conflict

Evidence may conflict.

AEM should identify:

which evidence conflicts,

what attribution claim is affected,

whether the conflict can be resolved,

and how unresolved conflict changes confidence.

Conflict should not be silently averaged away.

37. Evidence Independence

Multiple evidence items may originate from the same underlying source.

AEM should distinguish:

multiple records

from

multiple independent evidence sources.

Evidence Quantity ≠ Evidence Independence.

38. Evidence Corroboration

Independent evidence may corroborate an attribution claim.

Corroboration can strengthen confidence where multiple evidence sources support the same material conclusion through sufficiently independent pathways.

39. Evidence Redundancy

Duplicate or substantially equivalent records should not artificially increase attribution confidence.

Repeated Evidence ≠ Stronger Evidence by default.

40. Evidence Reliability

Evidence Reliability concerns whether the evidence can reasonably be relied upon for the attribution purpose.

Reliability may depend on:

source integrity,

collection method,

system integrity,

observability,

consistency,

provenance,

and susceptibility to manipulation.

41. Evidence Quality

Evidence Quality should be assessed relative to the attribution claim.

Relevant dimensions may include:

relevance,

materiality,

specificity,

completeness,

coverage,

reliability,

provenance,

integrity,

temporal accuracy,

and reproducibility.

No universal scoring formula is required.

42. Evidence Sufficiency

Evidence is sufficient where it adequately supports the attribution claim at the level of precision and confidence being asserted.

Sufficiency depends on:

claim materiality,

claim scope,

attribution precision,

evidence quality,

uncertainty,

and unresolved conflict.

43. Evidence Sufficiency Threshold

Material attribution claims should have an explicit or methodologically defensible Evidence Sufficiency Threshold.

Higher-impact or more precise attribution claims may require stronger evidence.

Greater Attribution Precision requires greater evidentiary support.

44. Attribution Precision Constraint

AEM enforces:

Attribution Precision must not exceed Evidence Precision.

Where evidence supports only broad attribution, the resulting state should remain broad.

A percentage should not be generated merely because a system can calculate one.

45. False Precision Control

False Precision occurs where attribution is represented more precisely than the evidence can justify.

Examples include:

exact percentages derived from weak evidence,

fine-grained agent attribution from incomplete logs,

or precise Human–AI allocation without credible comparison.

AEM should downgrade unsupported precision.

46. Evidence-to-Weight Relationship

Where contribution weight is used, AEM should evaluate whether the evidence supports the stated weight.

Evidence that supports material contribution may not support an exact contribution percentage.

Evidence of Contribution ≠ Evidence of Exact Contribution Weight.

47. Attribution Confidence

AEM should establish confidence based on the evidentiary state.

Possible states may include:

High Confidence

Moderate Confidence

Conditional

Evidence-Limited

Low Confidence

Conflicted

Unresolved

Confidence should remain distinct from contribution magnitude.

48. Confidence Calibration

Confidence should reflect evidence rather than organizational preference.

AEM should prevent:

high confidence from weak evidence,

low confidence despite strong evidence without justification,

or confidence labels detached from defined criteria.

49. Attribution Uncertainty

AEM should preserve uncertainty arising from:

missing evidence,

partial observability,

complex interactions,

overlapping contribution,

counterfactual assumptions,

source ambiguity,

or unresolved conflict.

Uncertainty is part of the governed state, not an error to be hidden.

50. Known Unknowns

Where a material evidentiary gap is known, it should be explicitly recorded.

Examples may include:

missing agent logs,

unavailable human decision records,

unknown model version,

missing tool output,

or incomplete provenance.

Known Unknowns should affect attribution confidence where material.

51. Unknown Contribution Boundary

AEM should distinguish:

no evidence of contribution

from

evidence that no material contribution occurred.

Absence of Evidence ≠ Evidence of Absence.

This distinction is particularly important in partially observable systems.

52. Evidence Gap

An Evidence Gap exists where evidence required to support a material attribution claim is unavailable, incomplete, unreliable, or insufficiently specific.

Evidence Gaps should be associated with the claims they affect.

53. Critical Evidence Gap

A Critical Evidence Gap exists where the missing evidence prevents a material attribution conclusion from being governed with sufficient confidence.

The correct state may then be:

Evidence-Limited

or

Unresolved

rather than an unsupported definitive attribution.

54. Evidence Acquisition

Where an Evidence Gap can reasonably be resolved, AEM may require additional evidence acquisition.

This may include:

additional logging,

controlled comparison,

human documentation,

agent tracing,

version retrieval,

or another proportionate evidence mechanism.

AEM does not require evidence collection without regard to cost, privacy, security, or applicable constraints.

55. Evidence Proportionality

Evidence requirements should be proportionate to:

Value materiality,

attribution materiality,

decision relevance,

uncertainty,

and consequences of attribution error.

Minor attribution claims should not automatically require the same evidentiary burden as high-impact claims.

56. Evidence Collection Boundary

Evidence collection must remain within applicable:

privacy,

security,

legal,

ethical,

operational,

and governance constraints.

AEM does not authorize collection merely because additional evidence would improve attribution.

Evidence Need ≠ Evidence Collection Authority.

57. Evidence Preservation

Material attribution evidence should be preserved for an appropriate period consistent with the governed purpose and applicable constraints.

Preservation should support:

reconstruction,

challenge,

reassessment,

and version continuity.

58. Evidence Retention Relevance

Retention duration should reflect:

attribution purpose,

Value lifecycle,

decision relevance,

regulatory or contractual requirements where applicable,

and reassessment needs.

AEM does not independently establish legal retention obligations.

59. Evidence Traceability

AEM should preserve a traceable relationship:

Evidence Source → Evidence Object → Contribution Claim → Attribution Method → Attribution State

Traceability enables reconstruction of why an attribution was accepted.

60. End-to-End Attribution Traceability

Where practicable, the complete VAS evidence path should support:

Validated Value

↔ Value Source

↔ Contribution

↔ Human–AI / Multi-Agent Relationship

↔ Contribution Event

↔ Evidence

↔ Attribution Method

↔ Attribution State

This provides the evidentiary spine of VAS.

61. Evidence Reproducibility

A competent assessor should, where practicable, be able to reconstruct the material attribution reasoning from preserved evidence and methodology.

Reproducibility does not require identical judgment where genuine uncertainty exists.

It requires that the reasoning can be understood and tested.

62. Attribution Reconstruction

AEM should support reconstruction of:

what was attributed,

to whom or what,

on what evidence,

using which methodology,

with what confidence,

and under which assumptions.

If this cannot be reconstructed, attribution governance is materially weakened.

63. Evidence Explainability

Material attribution conclusions should be explainable at a level appropriate to the governed purpose.

Explainability should identify:

the claim,

material evidence,

methodological reasoning,

uncertainty,

and conclusion.

It does not require disclosure of information that cannot legitimately be disclosed.

64. Attribution Challenge

A material attribution should be capable of challenge where:

evidence is disputed,

a source is omitted,

contribution is misclassified,

weight is unsupported,

or methodology is contested.

Challenge creates a review condition.

It does not automatically invalidate the attribution.

65. Challenge Evidence

A challenge should itself be supported by relevant evidence or methodological reasoning where appropriate.

AEM should preserve:

challenge basis,

challenged claim,

supporting evidence,

response,

and resulting state.

66. Attribution Review

A review may confirm, modify, downgrade, suspend, or reopen an attribution state.

Review should preserve the reasoning and evidence supporting the outcome.

67. Independent Review

Where materiality warrants it, attribution evidence may be reviewed by a sufficiently independent assessor, process, or mechanism.

Independence requirements should be proportionate to the significance of the attribution.

68. Evidence Reassessment

Evidence should be reassessed where:

new evidence appears,

the Value object changes,

the contribution structure changes,

a material conflict emerges,

the methodology changes,

or previous evidence is invalidated.

69. Evidence Drift

Evidence Drift occurs where the evidentiary basis becomes less representative of the current attribution state over time.

Examples include:

changed model versions,

changed agent configurations,

changed human roles,

changed data sources,

or changed workflows.

A previously sufficient evidence state may become stale.

70. Attribution Evidence Freshness

Evidence freshness should be evaluated where contribution structures can materially change.

Old evidence may remain valid for historical attribution while becoming insufficient for current attribution.

Historical Evidence ≠ Current Attribution Evidence by default.

71. Evidence Versioning

AEM should preserve:

evidence version,

attribution version,

methodology version,

relevant system version,

and effective time

where material.

This supports reconstruction across changing attribution states.

72. Attribution Evidence Integrity

AEM should identify material integrity risks such as:

selective evidence,

omitted evidence,

altered records,

unsupported evidence weighting,

duplicated evidence,

missing provenance,

post-hoc reconstruction,

or evidence chosen solely to support a preferred attribution.

73. Selective Evidence Risk

Selective Evidence Risk occurs where evidence supporting one attribution interpretation is preserved while materially conflicting evidence is omitted.

AEM should require relevant conflicting evidence to remain visible.

74. Evidence Cherry-Picking

Evidence should not be selected solely because it supports a preferred contributor, attribution weight, or narrative.

AEM should preserve material evidence both for and against contested attribution claims.

75. Attribution Narrative Bias

An organization may prefer descriptions such as:

“human-created,”

“AI-generated,”

“fully automated,”

“agent-produced,”

or

“human-controlled.”

AEM should test whether evidence supports the narrative rather than allowing the narrative to determine the evidence interpretation.

76. Evidence Quantity Bias

Large evidence volume may create an appearance of certainty.

But:

More Evidence ≠ Better Evidence by default.

Evidence quality and relevance remain decisive.

77. Logging Bias

Systems with more extensive logging may appear to contribute more simply because their activity is more visible.

AEM should distinguish:

Contribution Visibility

from

Contribution Materiality.

Poor observability should increase uncertainty, not automatically erase contribution.

78. Attribution Evidence State

AEM may represent evidentiary states such as:

Sufficient

Strongly Supported

Conditionally Sufficient

Partially Supported

Evidence-Limited

Materially Conflicted

Insufficient

Stale

Integrity-Constrained

Unresolved

These states describe evidence support, not contribution magnitude.

79. Evidence and Attribution State Relationship

An attribution state should remain constrained by its evidence state.

For example:

highly precise attribution should not coexist with materially insufficient evidence without explicit qualification.

AEM therefore maintains:

Attribution Claim ≤ Evidentiary Support

as a governing relationship.

80. Evidence and Value Change

If the underlying Value is materially revised or invalidated, the evidence supporting its attribution may require reassessment.

Evidence supporting attribution to one Value state should not automatically support attribution to another.

81. Evidence and Human–AI Change

Where Human–AI roles change materially, historical evidence may no longer support current Human–AI attribution.

AEM should preserve the applicable configuration and time period.

82. Evidence and Multi-Agent Change

Where agents, roles, routing, delegation, or coordination change materially, previous Multi-Agent attribution evidence may no longer remain sufficient.

Reconfiguration should trigger reassessment where attribution could materially change.

83. Evidence and Model Change

A model change may alter:

capability,

output,

reasoning behavior,

correction requirements,

or contribution relationships.

Attribution evidence should therefore preserve relevant model or system version where material.

84. Evidence and Workflow Change

Workflow redesign may change the source and contribution structure even where the final Value appears similar.

Evidence should correspond to the workflow actually producing the governed Value.

85. Evidence and Attribution Automation

Attribution analysis itself may be partially automated.

An automated system may:

collect evidence,

map contribution chains,

identify conflicts,

or propose attribution states.

Automated attribution remains subject to the same evidence requirements.

Automated Attribution ≠ Governed Attribution by default.

86. Evidence and Algorithmic Scoring

AEM may support algorithmic evidence scoring where methodologically appropriate.

But no score should replace the underlying evidence relationship.

A high score does not itself establish attribution truth.

87. Evidence and Confidence Automation

Confidence may be calculated or assisted algorithmically.

The method should remain:

documented,

bounded,

versioned,

and reviewable.

Automated confidence should not exceed the evidence available to the system.

88. Evidence Escalation

Where evidence remains materially insufficient for a consequential attribution claim, AEM may require escalation.

Escalation may lead to:

additional evidence,

human review,

independent review,

lower attribution precision,

conditional attribution,

or unresolved status.

89. Evidence Downgrade

An existing attribution may require downgrade where:

supporting evidence becomes invalid,

provenance fails,

material conflict emerges,

or system changes make historical evidence insufficient.

Downgrade preserves integrity.

It is not evidence-system failure.

90. Evidence Recovery

Where evidence has been incomplete or degraded, a recovery process may attempt to reconstruct the attribution basis.

Recovered evidence should remain distinguishable from contemporaneously captured evidence where that difference affects reliability.

91. VAS Evidence Consolidation

As the final VAS module, AEM consolidates evidence associated with:

Value Source Identification

Contribution Attribution

Human–AI Attribution

Multi-Agent Attribution

The consolidation should preserve each upstream state rather than flatten them into a single unexplained attribution result.

92. Governed Attribution Evidence State

The principal module output is:

Governed Attribution Evidence State

The state should identify, where applicable:

Attribution Claims,

supporting evidence,

evidence type,

evidence source,

evidence origin,

provenance,

relevance,

materiality,

specificity,

completeness,

coverage,

consistency,

conflict,

independence,

corroboration,

reliability,

quality,

sufficiency,

Evidence Gaps,

Critical Evidence Gaps,

confidence,

uncertainty,

traceability,

reproducibility,

challenge state,

review state,

freshness,

integrity,

and version.

93. VAS Consolidation

AEM supports consolidation of the complete VAS internal flow:

Governed Value Source State

↓

Governed Contribution Attribution State

↓

Governed Human–AI Attribution State

↓

Governed Multi-Agent Attribution State

↓

Governed Attribution Evidence State

↓

Governed Value Attribution State

The consolidated state represents the governed methodological attribution of validated Value.

94. Attribution State Integrity

The final Governed Value Attribution State must remain consistent with the evidence supporting it.

AEM should prevent:

unsupported precision,

unsupported confidence,

unresolved conflicts presented as settled,

evidence gaps presented as complete evidence,

and historical evidence presented as current without justification.

95. Decision Handoff Integrity

The Governed Value Attribution State may become an input to Decision Governance.

The handoff should preserve:

attribution state,

evidence state,

confidence,

uncertainty,

material conflicts,

and unresolved Evidence Gaps.

A downstream decision should not receive a cleaner attribution state than the evidence actually supports.

96. Minimum Implementation Framework

A conforming implementation of AEM must provide exactly the following six capabilities:

1. Attribution Claim & Evidence Object Governance

Capability to define Attribution Claims, Evidence Objects, evidence scope, relevance, materiality, and claim-to-evidence relationships.

2. Evidence Quality, Sufficiency & Precision Governance

Capability to evaluate evidence specificity, completeness, coverage, consistency, independence, corroboration, reliability, quality, sufficiency, Evidence Sufficiency Thresholds, and Attribution Precision constraints.

3. Evidence Provenance, Traceability & Reproducibility Governance

Capability to preserve evidence identity, origin, provenance, temporal integrity, version integrity, end-to-end traceability, reconstruction, reproducibility, and appropriate explainability.

4. Evidence Gap, Uncertainty & Challenge Governance

Capability to identify Evidence Gaps, Critical Evidence Gaps, known unknowns, confidence, uncertainty, conflict, challenge, review, reassessment, escalation, downgrade, and recovery.

5. Attribution Evidence Integrity & Lifecycle Governance

Capability to govern evidence collection boundaries, preservation, freshness, drift, selective evidence risk, narrative bias, logging bias, automation, revision, versioning, and attribution evidence integrity.

6. VAS Consolidation & DGS Handoff Governance

Capability to consolidate all VAS module outputs into a Governed Value Attribution State and preserve the evidence conditions required for subsequent Decision Governance.

97. Governance Logic

The canonical AEM governance logic is:

Governed Multi-Agent Attribution State

↓

Identify Attribution Claims

↓

Identify Supporting Evidence Objects

↓

Establish Claim-to-Evidence Relationships

↓

Assess Evidence Scope / Relevance / Materiality

↓

Classify Direct / Indirect / Observed / Inferred / Counterfactual Evidence

↓

Assess Human / AI / Multi-Agent / Tool / Data / Infrastructure / Methodology Evidence

↓

Verify Evidence Identity / Origin / Provenance

↓

Assess Temporal / Version Integrity

↓

Assess Completeness / Coverage / Specificity

↓

Assess Consistency / Conflict

↓

Assess Independence / Corroboration / Redundancy

↓

Assess Reliability / Quality

↓

Determine Evidence Sufficiency

↓

Test Attribution Precision against Evidence Precision

↓

Identify Evidence Gaps / Critical Evidence Gaps

↓

Assess Confidence / Uncertainty

↓

Verify Traceability / Reproducibility / Reconstruction

↓

Enable Challenge / Review where required

↓

Assess Freshness / Drift

↓

Test Evidence Integrity / Bias

↓

Establish Governed Attribution Evidence State

↓

Consolidate VAS Module States

↓

Establish Governed Value Attribution State

↓

Handoff to Decision Governance

98. Governance Outputs

AEM may produce:

Attribution Claim Record

Attribution Evidence Object

Claim-to-Evidence Link

Evidence Scope State

Evidence Relevance State

Evidence Materiality State

Direct Attribution Evidence Record

Indirect Attribution Evidence Record

Observed Evidence Record

Inferred Evidence Record

Counterfactual Evidence Record

Comparative Evidence Record

Experimental Evidence Record

Operational Evidence Record

Human Attribution Evidence Record

AI Attribution Evidence Record

Multi-Agent Attribution Evidence Record

Tool Attribution Evidence Record

Data Attribution Evidence Record

Infrastructure Attribution Evidence Record

Methodology Attribution Evidence Record

Evidence Identity Record

Evidence Origin Record

Evidence Provenance Record

Evidence Temporal Integrity State

Evidence Version Integrity State

Evidence Integrity State

Evidence Completeness State

Evidence Coverage State

Evidence Specificity State

Evidence Consistency State

Evidence Conflict State

Evidence Independence State

Evidence Corroboration State

Evidence Redundancy State

Evidence Reliability State

Evidence Quality State

Evidence Sufficiency State

Evidence Sufficiency Threshold

Attribution Precision Constraint

False Precision State

Evidence-to-Weight State

Attribution Confidence State

Attribution Uncertainty State

Known Unknown Record

Evidence Gap Record

Critical Evidence Gap Record

Evidence Acquisition Requirement

Evidence Proportionality State

Evidence Preservation Record

Evidence Traceability Record

End-to-End Attribution Trace

Evidence Reproducibility State

Attribution Reconstruction Record

Attribution Challenge Record

Attribution Review State

Evidence Reassessment State

Evidence Drift State

Evidence Freshness State

Evidence Version

Attribution Evidence Integrity State

VAS Consolidation Record

The principal module output is:

Governed Attribution Evidence State

The consolidated Parent Standard output is:

Governed Value Attribution State

99. Critical Distinctions

AEM preserves the following distinctions:

Evidence Availability ≠ Attribution Evidence Sufficiency

Evidence Quantity ≠ Evidence Quality

Evidence Quantity ≠ Evidence Independence

Repeated Evidence ≠ Stronger Evidence by default

Direct Evidence ≠ Sufficient Evidence by default

Indirect Evidence ≠ Weak Evidence by default

Inference ≠ Observation

Counterfactual Estimate ≠ Observed Outcome

Operational Success ≠ Contribution Evidence

Human Identity ≠ Human Contribution Evidence

AI Invocation ≠ AI Contribution Evidence

Tool Invocation ≠ Tool Value Contribution

Data Availability ≠ Data Contribution Evidence

Resource Consumption ≠ Infrastructure Value Contribution

Evidence of Contribution ≠ Evidence of Exact Contribution Weight

Attribution Precision must not exceed Evidence Precision

More Evidence ≠ Better Evidence by default

Absence of Evidence ≠ Evidence of Absence

Uncertainty ≠ Attribution Failure

Evidence Gap ≠ Zero Contribution

Historical Evidence ≠ Current Attribution Evidence by default

Contribution Visibility ≠ Contribution Materiality

Automated Attribution ≠ Governed Attribution by default

Evidence Need ≠ Evidence Collection Authority

Attribution Evidence ≠ Legal Proof by default

Attribution Evidence ≠ Decision Authority

100. Cross-Architecture Boundary

AEM governs evidence specifically for methodological Value Attribution within VAS.

VSIM identifies Value sources.

CATM governs Contribution Attribution.

HAAM governs Human–AI Attribution.

MAAM governs Multi-Agent Attribution.

AEM governs whether those attribution states are sufficiently supported by evidence and consolidates them into the final VAS output.

AEM does not replace broader evidence, audit, validation, legal, or institutional governance.

ADIT® may govern broader evidence and auditability architecture.

VALIDOS™ may govern validation methodology.

Relevant identity architectures may govern contributor and source identity.

HAGA™ governs Harm.

LIGA™ governs Liability.

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 Decision Governance.

AEM may consume or produce interoperable evidence states without absorbing those governed domains.

101. Canonical Boundary

AEM begins when VAS contains material attribution claims requiring evidentiary governance.

AEM governs:

Attribution Claims,

Attribution Evidence Objects,

claim-to-evidence relationships,

evidence scope,

relevance,

materiality,

direct evidence,

indirect evidence,

observed evidence,

inferred evidence,

counterfactual evidence,

comparative evidence,

experimental evidence,

operational evidence,

Human Attribution Evidence,

AI Attribution Evidence,

Multi-Agent Attribution Evidence,

tool evidence,

data evidence,

infrastructure evidence,

methodology evidence,

evidence identity,

origin,

provenance,

temporal integrity,

version integrity,

integrity,

completeness,

coverage,

specificity,

consistency,

conflict,

independence,

corroboration,

redundancy,

reliability,

quality,

sufficiency,

Evidence Sufficiency Thresholds,

Attribution Precision constraints,

False Precision,

Evidence-to-Weight relationships,

confidence,

uncertainty,

Known Unknowns,

Evidence Gaps,

Critical Evidence Gaps,

evidence acquisition,

proportionality,

collection boundaries,

preservation,

retention relevance,

traceability,

end-to-end attribution traceability,

reproducibility,

reconstruction,

explainability,

challenge,

review,

independent review,

reassessment,

drift,

freshness,

versioning,

selective evidence risk,

Evidence Cherry-Picking,

Attribution Narrative Bias,

Evidence Quantity Bias,

Logging Bias,

evidence escalation,

downgrade,

recovery,

VAS evidence consolidation,

Attribution State Integrity,

and Decision Handoff Integrity.

AEM does not independently determine:

legal admissibility,

legal proof standards,

legal ownership,

intellectual-property rights,

compensation,

tax treatment,

Accountability,

Responsibility,

Harm,

Liability,

privacy authority,

evidence collection authority,

or final lifecycle Decision Authority.

AEM ends when attribution evidence has been represented as a:

Governed Attribution Evidence State

and the complete VAS module chain has been consolidated into a:

Governed Value Attribution State

for subsequent Decision Governance.

102. Use Case 1 — The Company Claims 80% AI Contribution

An organization reports that an AI system created 80% of the Value associated with a new operational process.

The percentage was derived primarily from the proportion of final text generated by AI.

AEM evaluates the evidence supporting the claim.

The evidence shows that:

AI generated most of the visible output,

humans defined the problem,

a human expert supplied decisive domain information,

the AI generated an initially incorrect recommendation,

a human corrected the error,

and the corrected recommendation produced the validated Value.

The available evidence may support substantial AI contribution.

It does not necessarily support an exact claim of 80%.

AEM therefore constrains the attribution to the level of precision justified by the evidence.

Evidence of Contribution ≠ Evidence of Exact Contribution Weight.

103. Use Case 2 — Multi-Agent Attribution Looks Precise but the Logs Are Incomplete

A multi-agent system reports contribution percentages for eight agents.

The system records detailed outputs from six agents.

Two agents operate through external services for which only partial execution records exist.

One of those agents may have supplied information materially affecting the final result.

The attribution engine nevertheless assigns exact percentages across all eight agents.

AEM identifies a Critical Evidence Gap.

The correct governed state may require:

lower attribution precision,

explicit uncertainty,

conditional attribution,

additional evidence acquisition,

or an unresolved contribution state.

The missing logs do not prove that the two agents contributed nothing.

Absence of Evidence ≠ Evidence of Absence.

104. Canonical Closing Statement

Attribution becomes governable only when its evidence can withstand examination.

A contribution can be plausible without being proven.

A contributor can be visible without being material.

A system can produce thousands of records while still failing to evidence the claim that matters.

And an attribution percentage can look precise while resting on evidence incapable of supporting that precision.

Evidence governance must therefore constrain attribution rather than decorate it.

Evidence Availability ≠ Attribution Evidence Sufficiency.

Evidence Quantity ≠ Evidence Quality.

Inference ≠ Observation.

Absence of Evidence ≠ Evidence of Absence.

Evidence of Contribution ≠ Evidence of Exact Contribution Weight.

Attribution Precision must not exceed Evidence Precision.

AEM does not ask whether an attribution sounds reasonable.

It asks whether the attribution can be supported, traced, reconstructed, challenged, and maintained at the level of certainty it claims.

AEM closes the VAS attribution chain by ensuring that Value is attributed only as precisely, confidently, and definitively as the evidence can justify.