IBM Watson Studio Explainable AI
Increase AI interpretability. Assess and mitigate AI risks. Deploy AI with trust and confidence.
IBM Watson Studio Explainable AI Capabilities
Monitor and Explain Models
Prepare and configure model monitors. Provide model input including training data, training data labels and training features. Select your data and models to deploy.
Track and Visualize Model Insights
Select model metrics such as fairness, quality, drift, risk and performance. Explore debiasing options. Create preproduction and production repositories and compare models.
Configure Endpoint Monitors
Use a payload logging endpoint. Capture scoring requests or evaluate models for quality. Generate a code snippet for the payload and feedback endpoints to integrate them into your application.
IBM Watson Studio Explainable AI Case Studies
From one year to six weeks
Learn how Highmark Health uses model insights to predict patients at risk, supporting preventative clinical intervention and outreach.
Scaling AI at Lufthansa
Learn how Lufthansa achieved in customer experience and airline efficiency with AI lifecycle automation and drift and bias mitigation.
KPMG: Stewarding responsible AI
Learn how KPMG uses IBM to bring trust, transparency and explainability to production AI.
IBM Watson Studio Explainable Use Cases
Healthcare
Accelerate diagnostics, image analysis, resource optimization and medical diagnosis. Improve transparency and traceability in decision-making for patient care. Streamline the pharmaceutical approval process with explainable AI.
Financial Services
Improve customer experiences with a transparent loan and credit approval process. Speed credit risk, wealth management and financial crime risk assessments. Accelerate resolution of potential complaints and issues. Increase confidence in pricing, product recommendations and investment services.
Criminal Justice
Optimize processes for prediction and risk assessment. Accelerate resolutions using explainable AI on DNA analysis, prison population analysis and crime forecasting. Detect potential biases in training data and algorithms.
