Operationalize AI Fairness in Healthcare

Try the most complete ML model monitoring, bias detection, and explainability solution in healthcare today

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One Dashboard for All Your Models

Track the performance of all your healthcare models in a unified dashboard

Ensure Fairness Across Segments

Identify biases against protected classes in your models using univariate or multivariate segmentation

Identify Important Features

Identify the most important features in determining the outcomes of your healthcare models

Explainability and Bias Monitoring for Your Healthcare Models

Ensure the fairness & robustness of your ML models

Improve robustness of healthcare logistics models used for hospital management or capacity planning

Detect biases in your medical diagnosis models by uncovering differences in accuracy and other performance metrics across different subgroups

Identify the most important words in classifying medical documents using NLP explainability feature

arthur explainability inferences what-if scenario gif

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Want a demo of how Arthur can enable you to build better AI models and detect fraud more effectively? Understand how to build an explainable fraud detection system to save time, reduce costs, and increase trust with explainable AI tools and proper AI model governance.

Download Our Healthcare Case Study

As a healthcare organization, Humana’s top priority is making sure that its 22 million members have the best possible care and health outcomes, and it’s leaning on AI to help it accomplish that mission. Central to that challenge is ensuring health equity, particularly as it pertains to levels of care across underrepresented and minority groups.

Learn how Humana relies on Arthur's model monitoring software to help it reduce the risk of potential harm from its AI models and to address their fairness and biases.

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Operationalize AI Fairness in Healthcare

Explainability and Bias Monitoring for Your Healthcare Models

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