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Audit the personalization models for systematic bias in who they exclude, accelerate, or under-serve, so the model's behavior is defensible per protected category and per business-sensitive segment
Resolve several people at one address into a household where the buying unit is the household, without letting one member's data quietly drive another's experience
Use probabilistic signals when deterministic identifiers are absent, carrying the confidence bound forward so the systems acting on the match know whether they are reading a high-confidence resolution or a guess