Evidence for safe and fair AI systems is possible as long as you define what "safe" and "fair" mean for your usecase. Fairness might look like "no cohort has >5% higher false positive rate than another" and safety might mean "the model must have a false negative rate of less than 15%". Safety more so encompasses the workflows around the model, including human intervention, auditing, monitoring, etc.

Here's a good overview of fairness: https://learn.microsoft.com/en-us/azure/machine-learning/con... and there's plenty of papers discussing how to safely use predictive analytics and AI in healthcare.

I don't know if this product can give proof for safe and fair ML systems, but it's not impossible to use these things safely and fairly.

Thanks for your question. Parachute's workflows are built around widely accepted conventions for safety and fairness for AI models in healthcare such as NIST AI RMF, CHAI and HAIP's HEAAL framework.

You're right; we should not even try. Better to have 0% compliance coverage and your honor than 90% coverage as best-effort.

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