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Responsible Use of AI in Human Resources for Risk Management: Top 3 Trends & Use Cases

While AI technology yields significant operational benefits for HR departments, it also introduces risk. Any company using AI systems that analyze protected, special categories or sensitive personal datasets needs to ensure the data and/or algorithms being used are not causing bias, discrimination, or disparate impact.

In this guide, learn about the top three use cases for AI in HR, existing and upcoming regulation, as well as how to balance the rewards vs. risks of using automated AI platforms across the HR lifecycle.

Arthur for Human Resources

Learn how Arthur can reduce HR risk for your business and improve processes such as recruiting, hiring, performance management and employee experience.

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.

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“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:

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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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