SHAP
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.
SHapley Additive exPlanations — game-theoretic feature attribution for ML model explainability, transforms black-box algorithms into transparent systems respecting user right to understand automated decisions
No foundation model. SHAP does not run one, so this criterion does not apply and is excluded from the grade rather than counted against it. A game-theoretic post-hoc explainability library that computes Shapley-value feature attributions for a user's own trained machine learning model; it implements algorithms and ships no foundation model weights or hosted inference of its own. 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 If that is out of date, tell us at right of reply.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.
Taken from the vendor’s own published material. Vannus does not hold these reports and has not reviewed their scope or dates — ask the vendor for the current report before relying on any of them.
Vannus publishes a nine-dimension trust framework — data sovereignty, training privacy, compliance posture, operational resilience, exit portability, and more. The heaviest criterion asks whether the tool builds its own AI or resells someone else's model; where the vendor discloses it, the grade cites the vendor's own documentation. No paid placements — scoring is walled off from affiliate revenue. See the methodology →