How to Build Privacy-Preserving Evaluation Benchmarks with Synthetic Data

Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety…

Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety before deployment. Without them, you’re guessing. But in regulated domains such as healthcare, finance, and government, data scarcity and privacy constraints make building benchmarks incredibly difficult. Real-world data is locked behind…

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