SHAP documents beeswarm and bar plots for aggregate views across explanations; these are model-behavior summaries, not causal findings.
Inspect the evidence ↘GOVERNATLAS / PRODUCT DOSSIER
SHAP
The SHAP project documents a game-theoretic approach to explaining machine-learning model outputs through feature attributions.
THE DOCUMENTED SCOPE
What the current evidence describes.
Source-attributed capabilities and limits. Inspect the full record before drawing a conclusion.
Its README includes tree ensemble, natural language and deep-learning explanation examples; compatibility and assumptions depend on the selected explainer.
Inspect the evidence ↘SHAP documents feature attributions connecting a reference value to a model output, including waterfall and force visualizations.
Inspect the evidence ↘01 / CONTEXT & SCOPE
Understand the proposition.
Define the boundary.
The decision to make
State what the attribution is relative to. The selected explainer, background data, output scale and feature dependencies affect the interpretation. A feature contribution is not a demonstration that intervening on the real-world feature would cause the outcome to change, and a global ranking does not replace a case-level explanation.
What this record does not establish
Feature-attribution documentation does not establish causal effects, a correct reference population, fairness or an appropriate explanation for every reader. Pin the implementation and dependency versions; no model was run for this profile.
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
Keep a synthetic routing model and target record fixed while comparing two documented background cohorts. Retain the prediction and both attributions, repeat approximate calculations where appropriate and ask a reviewer to explain a changed ranking without calling it a changed model.
GovernAtlas editorial proposal. We have not performed this test.
B
Evidence to bring back
- An explainer/background/output-scale/feature-schema record.
- A reproducible case explanation with a reference-cohort sensitivity review.
THE OPERATING DETAIL
A contribution needs its reference and output scale.
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
SHAP documents beeswarm and bar plots for aggregate views across explanations; these are model-behavior summaries, not causal findings.
Source, dates & preserved provenance
SHAP — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: afb60626290a664183adec8dc333ef99b4eb6bf583b6934b2f984d582158c923
The hash identifies the preserved capture. The live source may have changed.
first party asserted
Its README includes tree ensemble, natural language and deep-learning explanation examples; compatibility and assumptions depend on the selected explainer.
Source, dates & preserved provenance
SHAP — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: afb60626290a664183adec8dc333ef99b4eb6bf583b6934b2f984d582158c923
The hash identifies the preserved capture. The live source may have changed.
first party asserted
SHAP documents feature attributions connecting a reference value to a model output, including waterfall and force visualizations.
Source, dates & preserved provenance
SHAP — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: afb60626290a664183adec8dc333ef99b4eb6bf583b6934b2f984d582158c923
The hash identifies the preserved capture. The live source may have changed.
first party asserted
The SHAP project documents a game-theoretic approach to explaining machine-learning model outputs through feature attributions. Scope: primary documentation only; no independent model test, fairness determination, causal finding or compliance approval.
Source, dates & preserved provenance
SHAP — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: afb60626290a664183adec8dc333ef99b4eb6bf583b6934b2f984d582158c923
The hash identifies the preserved capture. The live source may have changed.
Source register
SHAP — 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 an explainer/background/output-scale/feature-schema record for the selected configuration?
Can you provide a reproducible case explanation with a reference-cohort sensitivity review 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.