Its listed proxy explanation metrics include faithfulness and monotonicity. The README points to a separate ICX360 toolkit for LLM input explanation; that scope is not attributed to AIX360.
Inspect the evidence ↘GOVERNATLAS / PRODUCT DOSSIER
AI Explainability 360
The AI Explainability 360 project documents an open-source toolkit for explaining datasets and machine-learning models.
THE DOCUMENTED SCOPE
What the current evidence describes.
Source-attributed capabilities and limits. Inspect the full record before drawing a conclusion.
Its taxonomy distinguishes data and model explanations, direct and post-hoc methods, and local and global views.
Inspect the evidence ↘AIX360 documents explainability algorithms and proxy metrics for tabular, text, image and time-series data.
Inspect the evidence ↘01 / CONTEXT & SCOPE
Understand the proposition.
Define the boundary.
The decision to make
Use the explanation taxonomy to choose a method rather than treating a collection of algorithms as one assurance claim. Local, global, data and model explanations have different consumers and assumptions. The README also points to a separate newer in-context toolkit; do not transfer that toolkit’s LLM functionality to this profile.
What this record does not establish
The toolkit is distinct from AI Fairness 360 and the separately referenced ICX360 project. Proxy explanation metrics, available algorithms and sample notebooks do not establish causal validity, fairness or current compatibility of every optional dependency.
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
Choose a synthetic model and one explanation question, pin the selected algorithm and its dependency environment, and retain the explanation output. Ask a second reviewer to interpret it using only the manifest, then record an ambiguity that requires follow-up rather than declaring the model explained.
GovernAtlas editorial proposal. We have not performed this test.
B
Evidence to bring back
- A chosen algorithm/reader/question/dependency manifest.
- An explanation artifact with reviewer interpretation and unresolved limits.
THE OPERATING DETAIL
Choose the explanation before choosing its presentation.
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 listed proxy explanation metrics include faithfulness and monotonicity. The README points to a separate ICX360 toolkit for LLM input explanation; that scope is not attributed to AIX360.
Source, dates & preserved provenance
AI Explainability 360 — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: 3f1fc27b95f3b63cab0592cf18cbca6b1229da96aa566f6207b1d0d9d174e5df
The hash identifies the preserved capture. The live source may have changed.
first party asserted
Its taxonomy distinguishes data and model explanations, direct and post-hoc methods, and local and global views.
Source, dates & preserved provenance
AI Explainability 360 — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: 3f1fc27b95f3b63cab0592cf18cbca6b1229da96aa566f6207b1d0d9d174e5df
The hash identifies the preserved capture. The live source may have changed.
first party asserted
AIX360 documents explainability algorithms and proxy metrics for tabular, text, image and time-series data.
Source, dates & preserved provenance
AI Explainability 360 — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: 3f1fc27b95f3b63cab0592cf18cbca6b1229da96aa566f6207b1d0d9d174e5df
The hash identifies the preserved capture. The live source may have changed.
first party asserted
The AI Explainability 360 project documents an open-source toolkit for explaining datasets and machine-learning models. Scope: primary documentation only; no independent model test, fairness determination, causal finding or compliance approval.
Source, dates & preserved provenance
AI Explainability 360 — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: 3f1fc27b95f3b63cab0592cf18cbca6b1229da96aa566f6207b1d0d9d174e5df
The hash identifies the preserved capture. The live source may have changed.
Source register
AI Explainability 360 — 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 chosen algorithm/reader/question/dependency manifest for the selected configuration?
Can you provide an explanation artifact with reviewer interpretation and unresolved limits 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.