Build trust, ensure compliance, and drive more actionable ML outcomes with Arthur’s explainability features.
"Arthur is 6-9 months ahead of the competition and there was a clear preference for their UX among our data scientists."
Understand model importance across types: including feature importance for tabular models, word importance for NLP models, and image region importance for CV models.
Leverage context around model predictions and what-if simulations to drive stronger, more actionable outcomes.
Explore model behavior, patterns and investigate problems with global, regional, & individual explanations while provide complete model transparency.