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Training on a subset of data, a common technique for scaling ML, surprisingly introduces new privacy vulnerabilities by leaking information about both the training set and the selection process itself.
Agentic data science pipelines often reach falsely optimistic conclusions, but two simple sanity checks can expose these unsupported claims by testing if the agent can reliably distinguish signal from noise.
