What is IBM Watson OpenScale?

Watson OpenScale tracks and measures outcomes from AI across its lifecycle, and adapts and governs AI to changing business situations - for models built and running anywhere.


Measure and track AI outcomes

Track performance of production AI and its impact on business goals, with actionable metrics in a single console.

Tune your AI for business

Apply business results to create a continuous feedback loop that improves and sustains AI outcomes.

Govern and explain AI

Maintain regulatory compliance by tracing and explaining AI decisions across workflows, and intelligently detect and correct bias to improve outcomes.

Use Cases

Credit Risk Modeling

Credit lenders can monitor risk models for performance, bias, and explainability to limit risk exposure from regulations, and create more fair and explainable outcomes for customers.

Credit Risk Modeling


Explainable claims processing

Insurance underwriters can use machine learning to more consistently and accurately assess claims risk, ensure fair outcomes for customers and explain AI recommendations for regulatory and business intelligence purposes.

Explainable claims processing


Predict CSP asset failures

Data scientists can build machine-learning models and work with their IT operations teams to confidently recommend proactive asset maintenance for communications service providers (CSPs).

Predict CSP asset failures



Explore reference material to get started and see what you can do in Watson OpenScale.

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Watson OpenScale Tutorial

Learn how to configure Watson OpenScale and set up bias detection and explainability.

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Expert Insights

IBM Research Explainable AI

Generate contrastive explanations for any classification model with MACEM

IBM Research : Bias Mitigation

Increase fairness of predictions and improve accuracy, by following this new framework

Pund-IT: Explainable AI and IBM

"IBM's work in explainable AI should improve...acceptance and adoption of artificial intelligence," according to Pund-IT. Learn more