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Data minimization in AI products: getting both usefulness and compliance
The more a model 'knows you', the better it feels — but data-minimization principles ask for the opposite. The balance lies in purpose and retention.
Personalization is central to AI product experience, yet data-protection laws broadly require 'minimum necessary' and 'purpose limitation'. The apparent conflict can be resolved by design.
The first step is purpose separation: split data 'necessary to provide the service' from data 'used to improve the model'; the latter usually needs separate, revocable consent.
The second is retention and de-identification: set different retention periods per purpose, and prefer de-identified or aggregated data for training and analytics.
The third is explainability and control: let users see what data drives a decision and offer a switch to turn off personalization. Compliance need not cost experience.