Stay on top of healthcare AI bias risk

Fairsense is an AI bias risk observability platform designed for healthcare and cross-functional governance.

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A screenshot of fairness analysis results showing a low risk score for training dataset bias and high risk for model fairness. The screenshot is overlayed on top of a 3-dimensional graph structure, abstractly representing multi-stakeholder collaboration.

Striving for fairness in healthcare AI leads to better patient safety and outcomes

Customer Trust & Retention
AI Development Standards
Regulatory Compliance
AI Fairness in Healthcare
Learn more about AI fairness

Be compliant and enhance trust through efficient & cross-functional bias management and mitigation

Getting AI fairness right in healthcare requires clinical, legal, and ethical inputs. The Fairsense Health Platform guides your team through defining fairness, to quantifying and identifying areas of bias, so that you can apply targeted mitigations to reduce bias risk.

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AI/ML
Focus on core development instead of doing manual and monotonous analyses for multiple models and model variations.
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Product
‍Be notified of potential biases and flag performance issues to dev teams early.
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Clinical
Confirm clinical nuances and potential harms, and see how they can impact the fairness assessment of your models.
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Legal
Understand the technical components of fairness and how they translate to legal compliance and risk.

How it works

The Fairsense Health Platform translates qualitative fairness configurations into the right set of statistical tests and reports that are easy to interpret and share with technical and less technical stakeholders.
Fairsense Platform - Create an analysis
  1. For each AI/ML model, our Platform guides your cross-functional teams through defining fairness based on considering potential harms, sensitive groups, clinical control factors and more. Then those inputs are tracked for compliance records.
  2. Using these inputs, our Platform recommends appropriate statistical methods to quantify fairness and identify areas of bias. Fairness is measured on data representation and outcome fairness. 
Fairsense Health - Fairness Monitoring
  1. Our Platform recommends mitigation strategies to improve the fairness of your models and prepare a summary of what you’re doing to measure, monitor, and mitigate bias, which can be shared with external stakeholders and regulators.
  2. Through continuous monitoring, teams are notified of changes to fairness scores and are able to drill in deeper to understand what is affecting fairness over time.
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From one model to 1,000, Fairsense makes it easy for cross-functional teams to observe, de-risk, and report on the fairness of their models.

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Get to market faster with a more reliable product

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Save engineering & data science resources

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Ensure you are compliant with regulations & guidelines

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