Operationalize AI Fairness in Healthcare

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.

Download Our Case Study

This is a must-read for anyone trying to operationalize trustworthy, effective and fair AI at their organization.

Trusted by Fortune 100 Leaders

Fortune 100 leaders across financial services, healthcare, retail, and tech trust Arthur to monitor and improve their ML models to drive business impact.

“Thanks to Arthur, we know that our preventative care models are fair, and that we can catch any potential issues before they impact our members…and the Arthur platform allows us to detect and fix data drift before it becomes a real problem.”
— Chief Analytics Officer, Humana

”The biggest challenge is looking at this from a reputational risk perspective. The last thing we want is to be on the front page of the news with a bias issue.”
— Global Tech VP

“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

Monitor, measure, and improve ML models with Arthur

Arthur helps data scientists, product owners, and business leaders accelerate model operations at scale. We work with enterprise teams to measure and optimize model performance in production for:

Accuracy & Data Drift

Explainability & Transparency

Fairness & Bias Mitigation

Track model performance to detect and react to data drift, improving model accuracy for better business outcomes.

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

Proactively monitor for bias, track model outcomes against custom bias metrics, and improve the fairness of your models.

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