How does Owkin prevent bias in Owkin K recommendations?

We conduct bias audits during model development and testing. Our models are evaluated across diverse demographic datasets, and we partner with academic and clinical collaborators to validate performance in real world settings.

At Owkin, preventing bias in Owkin K’s recommendations is a central focus throughout the model development lifecycle. We conduct comprehensive bias audits during both development and testing phases, using a variety of statistical and qualitative checks to identify and mitigate potential sources of bias.

Our models are systematically evaluated across diverse demographic and clinical datasets to ensure that recommendations are robust and equitable for all patient groups. To further strengthen our safeguards, we collaborate closely with leading academic and clinical partners who independently assess Owkin K’s performance in real-world settings. Their feedback helps us refine our models and address any disparities that might emerge.

Additionally, we maintain ongoing monitoring after deployment, so that any new or unforeseen sources of bias can be quickly identified and corrected. Our commitment to transparency means that users can always trace the data sources and rationale behind K’s recommendations.

By combining rigorous technical evaluation with diverse, real-world validation and continuous monitoring, we strive to deliver recommendations that are fair, reliable, and trustworthy for everyone.