Fairlearn
This entry runs no foundation model. Every finding below is quoted to the vendor’s own document, or marked not disclosed where the vendor publishes nothing.
Microsoft fairness assessment and mitigation toolkit for ML — operationalises VSD fairness by quantifying demographic parity, equalized odds, and disparate impact in model predictions
No foundation model. Fairlearn does not run one, so there is no model provenance to record. An open-source Python toolkit of group-fairness metrics and bias- mitigation algorithms applied to a user's own trained model; it is assessment and mitigation code, not a model. Established from the product’s own public documentation and what it does. If that is out of date, tell us at right of reply.
Not yet assessed. We publish a sovereignty position only where the vendor documents one — we do not infer it from a domain or a company name.
Vannus records, from the vendor's own published documents, the legal entity a customer contracts with, the country that entity sits in, and the governing law of its terms — and whether the vendor runs its own model or resells someone else's. Each finding is quoted to its source and dated, or marked not disclosed where the vendor publishes nothing. No paid placement on a record, in the catalog or in search — affiliate status is walled off from the record, enforced by a test in the build. See the methodology →
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