STACKMODERNE
AMF evidence, provenance and research / GUIDE

AI model provenance: weights, data and claim limits

Document model provenance with pinned parent, dataset receipts, code, parameters, licenses and transformations.

ENGINEERING THESIS

Provenance explains origin and transformation; it does not replace evaluation or usage authority.

COMMON FAILURE

A weights hash without data origin, licensing or parameters leaves major enterprise unknowns.

DECISION

Verified, declared and unavailable information must be explicitly distinguished.

01 / REALIZATION

What the architecture must make explicit

Claims should terminate in inspectable evidence. Identity, provenance, measurement, adjudication, qualification and authority remain distinct dimensions.

01
Realization identity
02
Lineage and provenance
03
Reconstructible Evidence Package
04
Qualification and authority grant
02 / METHOD

Move from a keyword to an inspectable engineering decision

01

Identify

Compute the identity of the computation, artifacts and policies composing the realization.

02

Bind

Attach every measurement to the output actually produced by that exact subject.

03

Interpret

Separate observation, hypothesis, experimental support and terminal verdict.

04

Publish

Open useful projections without exposing protected data or widening claim scope.

03 / EVIDENCE GATES

A claim is only as strong as its binding

The gate must evaluate the exact output produced by the exact realization. Integrity, scientific validity and authority are related, but never interchangeable.

G1
Receipt integrity
G2
Admissible evidence role
G3
Frozen decision rule
G4
Explicit claim limits
FAQ

Questions that change the design

What does ai model provenance: weights, data and claim limits require?

Provenance explains origin and transformation; it does not replace evaluation or usage authority. Claims should terminate in inspectable evidence. Identity, provenance, measurement, adjudication, qualification and authority remain distinct dimensions.

Which evidence should be retained?

At minimum: exact realization identity, inputs and outputs, protocol version, measurements, failure cases and the decision rule. Verified, declared and unavailable information must be explicitly distinguished.

Does AMF prescribe a particular model?

No. AMF treats the model, runtime, data, tools and deterministic components as candidate materials. Their retention depends on measured contribution and declared constraints.