Key takeaways
- H2O Driverless AI writes an editable AutoDoc report for every experiment: data, validation scheme, feature engineering, final model performance and explanation plots.
- Machine learning interpretability gives reason codes for each prediction, plus Shapley values, surrogate decision trees, partial dependence and disparate impact analysis.
- Enterprise h2oGPTe answers your validators' evidence questions from AutoDocs, validation reports and policies, with a citation to the source page.
- The evidence supports the model risk process you already run; your model risk team still reviews, challenges and approves.
Every model has to answer the same questions
Your validators ask the same things of every model: what data trained it, which features drive the score, whether it was tested for disparate impact, and what changed since the last review.
The answers usually live in notebooks, emails, slide decks and a documentation file written weeks after the model was built. Rebuilding that file by hand is slow, and a validator who cannot find the evidence sends the model back.