Explainability

Build trust, ensure compliance, and drive more actionable ML outcomes with Arthur’s explainability features.

Build trust with valuable insights into how your ML models are making decisions.

Explore patterns, investigate problems, and ensure compliance with explanations for model outputs.

Improve trust in your models by better explaining why your machine learning model made a particular prediction, and see what predictions might be made with changes to model inputs.

"Arthur is 6-9 months ahead of the competition and there was a clear preference for their UX among our data scientists."

Head of Global Artificial Intelligence

Financial Services

Drive Explainability & Transparency with Arthur

Model Importance and Explanations

Understand model importance across types: including feature importance for tabular models, word importance for NLP models, and image region importance for CV models.

Counterfactuals

Leverage context around model predictions and what-if simulations to drive stronger, more actionable outcomes.

Model Explanations

Explore model behavior, patterns and investigate problems with global, regional, & individual explanations while provide complete model transparency.

Learn more about improving ML explainability with Arthur

Explainable AI
From Black Box to Glass Box: Transparency in XAI
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ML Explainability
Fast Counterfactual Explanations using Reinforcement Learning
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Explainable AI
Reinforcement Learning for Counterfactual Explanations
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