Its documented methods span preprocessing, in-processing and postprocessing, including group-specific threshold adjustments; no mitigation is established as appropriate for a real decision here.
Inspect the evidence ↘GOVERNATLAS / PRODUCT DOSSIER
Aequitas
Aequitas documents an open-source toolkit for bias auditing and fairness-aware machine learning.
THE DOCUMENTED SCOPE
What the current evidence describes.
Source-attributed capabilities and limits. Inspect the full record before drawing a conclusion.
The README describes Aequitas Flow experiments with configurable methods, runs and dataset splitting.
Inspect the evidence ↘Aequitas documents auditing model predictions from a pandas DataFrame containing labels, model outputs and categorical sensitive attributes.
Inspect the evidence ↘01 / CONTEXT & SCOPE
Understand the proposition.
Define the boundary.
The decision to make
Check the prediction and label semantics before interpreting a disparity plot. A categorical sensitive-feature column and a binary outcome encode a particular assessment question. Decide whether the task is auditing fixed predictions, applying a post-processing change or training a new pipeline; those actions require different evidence and responsibilities.
What this record does not establish
The toolkit description is not proof that a model is unbiased or that changing thresholds is suitable for a real decision. No sensitive-person eligibility determination or actual mitigation was performed 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
Prepare fictional binary routing predictions with a deliberate missing label and uneven cohort sizes. Inspect a selected audit metric, then consider a mitigation only on a separate held-out workflow. Preserve the original predictions and document which cases were excluded rather than presenting an automatically corrected result.
GovernAtlas editorial proposal. We have not performed this test.
B
Evidence to bring back
- A prediction/label/group schema and audit denominator record.
- An unchanged audit baseline and a separately reviewed mitigation experiment.
THE OPERATING DETAIL
Make the input table part of the review evidence.
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 documented methods span preprocessing, in-processing and postprocessing, including group-specific threshold adjustments; no mitigation is established as appropriate for a real decision here.
Source, dates & preserved provenance
Aequitas — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: 96e0dff3e5c8a44767a6727d24ee90052380d67a5c67018ec98ca86087f71336
The hash identifies the preserved capture. The live source may have changed.
first party asserted
The README describes Aequitas Flow experiments with configurable methods, runs and dataset splitting.
Source, dates & preserved provenance
Aequitas — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: 96e0dff3e5c8a44767a6727d24ee90052380d67a5c67018ec98ca86087f71336
The hash identifies the preserved capture. The live source may have changed.
first party asserted
Aequitas documents auditing model predictions from a pandas DataFrame containing labels, model outputs and categorical sensitive attributes.
Source, dates & preserved provenance
Aequitas — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: 96e0dff3e5c8a44767a6727d24ee90052380d67a5c67018ec98ca86087f71336
The hash identifies the preserved capture. The live source may have changed.
first party asserted
Aequitas documents an open-source toolkit for bias auditing and fairness-aware machine learning. Scope: primary documentation only; no independent model test, fairness determination, causal finding or compliance approval.
Source, dates & preserved provenance
Aequitas — primary scope and current identity ↗Authority: first party
Observed 2026-10-16 · Reviewed 2026-10-16 · current until 2026-11-15
Artifact SHA-256: 96e0dff3e5c8a44767a6727d24ee90052380d67a5c67018ec98ca86087f71336
The hash identifies the preserved capture. The live source may have changed.
Source register
Aequitas — 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 prediction/label/group schema and audit denominator record for the selected configuration?
Can you provide an unchanged audit baseline and a separately reviewed mitigation experiment 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.