External oversight
Built with scientists, for scientists
K-Pro's development is shaped by the people who use it. Biologists, translational scientists, and clinicians from academic and industry partners have taken part as early users and beta testers, reflecting Owkin's philosophy of "biologists building for biologists". Their feedback feeds directly into the product's capabilities, its interface, and the skills it offers, so that K-Pro fits the real workflows of scientific research.
Owkin also engages with the regulatory, academic, and clinical communities on the responsible use of AI in biomedical research, and draws on this dialogue in how K-Pro is designed and governed.
Scientific evaluation
Owkin evaluates K-Pro's scientific performance with domain experts, not only with automated tests. Owkin scientists review K-Pro's outputs on realistic research tasks, and the findings drive improvements to its tools, prompts, and skills. The goal is an honest, evidence-based picture of what K-Pro does well and where it still needs work, so that the commitments made to users stay realistic.
Approach to bias
Biomedical data reflect the populations they were collected from, and AI outputs can inherit those gaps. Owkin addresses this at several levels:
Know your data: K-Pro's datasets come with documented cohort composition, so you can see which populations an analysis covers. See About Owkin-managed data and the dataset catalog.
Transparency in every analysis: K-Pro states the cohort and filters it applied, so you can check whether a result may not generalize beyond the population studied.
Evaluation across contexts: K-Pro is evaluated on tasks spanning different indications, datasets, and data modalities.
Feedback after release: issues reported by users, including results that look biased or unrepresentative, are investigated and fed back into the product.
As with any analysis, results from a specific cohort may not generalize to all patient populations. Review cohort composition before you draw broad conclusions.
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