Its documentation describes input, neuron and layer attribution, as well as adversarial and minimal-input perturbation capabilities; these do not establish causal explanations.
Inspect the evidence ↘GOVERNATLAS / PRODUCT DOSSIER
Captum
Captum documents a model interpretability library for PyTorch, including attribution methods for inputs and model components.
THE DOCUMENTED SCOPE
What the current evidence describes.
Source-attributed capabilities and limits. Inspect the full record before drawing a conclusion.
The project describes concept- and training-example analysis using TCAV and TracIn influence functions.
Inspect the evidence ↘Captum documents PyTorch interpretability methods including Integrated Gradients, saliency and noise-based attribution methods.
Inspect the evidence ↘01 / CONTEXT & SCOPE
Understand the proposition.
Define the boundary.
The decision to make
Identify the prediction target, attribution method and baseline before comparing saliency views. Input, layer and neuron attribution expose different parts of a PyTorch model. A visually concentrated heatmap does not establish correctness or human meaningfulness, especially if preprocessing and the selected target are not retained.
What this record does not establish
The current official repository is meta-pytorch/captum; the older path redirects. Interpretability and perturbation capabilities do not establish causal truth, production robustness or universal model compatibility. No code was installed or run here.
1 current source · 4 published assertions
Source coverage is a research boundary, not a quality score.
Source coverage is a research boundary, not a quality score.
02 / YOUR EVALUATION
Turn interest into a useful trial.
A
A test worth running
Use a toy synthetic network with a known output path in an authorized environment. Compare two justified baselines under the same method, retain the prediction and attributions and inspect a method-appropriate sanity check. Keep any adversarial perturbation experiment outside the initial explanation review.
GovernAtlas editorial proposal. We have not performed this test.
B
Evidence to bring back
- A PyTorch/model/target/baseline/attribution-version record.
- A retained attribution and baseline-sensitivity or sanity-check observation.
THE OPERATING DETAIL
Make the attribution experiment reconstructible.
GovernAtlas analysis: practical review considerations, not findings from a product test.
03 / DOCUMENTED EVIDENCE
Read the claim. Inspect the basis.
Each statement retains its classification and preserved source. A vendor assertion is not an independent test result.
first party asserted
Its documentation describes input, neuron and layer attribution, as well as adversarial and minimal-input perturbation capabilities; these do not establish causal explanations.
Source, dates & preserved provenance
Captum — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: aa1b58a5d4a327dcd0ef4b824c578af6cde5a8bc41f52c8e97c0f609a96d5ca6
The hash identifies the preserved capture. The live source may have changed.
first party asserted
The project describes concept- and training-example analysis using TCAV and TracIn influence functions.
Source, dates & preserved provenance
Captum — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: aa1b58a5d4a327dcd0ef4b824c578af6cde5a8bc41f52c8e97c0f609a96d5ca6
The hash identifies the preserved capture. The live source may have changed.
first party asserted
Captum documents PyTorch interpretability methods including Integrated Gradients, saliency and noise-based attribution methods.
Source, dates & preserved provenance
Captum — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: aa1b58a5d4a327dcd0ef4b824c578af6cde5a8bc41f52c8e97c0f609a96d5ca6
The hash identifies the preserved capture. The live source may have changed.
first party asserted
Captum documents a model interpretability library for PyTorch, including attribution methods for inputs and model components. Scope: primary documentation only; no independent model test, fairness determination, causal finding or compliance approval.
Source, dates & preserved provenance
Captum — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: aa1b58a5d4a327dcd0ef4b824c578af6cde5a8bc41f52c8e97c0f609a96d5ca6
The hash identifies the preserved capture. The live source may have changed.
Source register
Captum — primary scope and current identity ↗first party · captured 2026-10-16
Brand asset provenance
Organization identity asset from its official website; inclusion does not imply endorsement.
Official website ↗ · Original asset ↗04 / BEFORE YOU COMMIT
Better questions.
A clearer decision.
Resolve these with the provider and your own evaluation team.
Can you provide a PyTorch/model/target/baseline/attribution-version record for the selected configuration?
Can you provide a retained attribution and baseline-sensitivity or sanity-check observation for the selected configuration?
Which exact version, entitlements, data paths and responsible roles apply?
Regulatory applicability requires a separate, current determination. Inclusion is not certification.
KEEP THE RECORD USEFUL
A dossier should stand up to scrutiny.
Have a source that changes this picture? Submit it for editorial review.