The Measured Child

A child cannot give informed consent to uses of their data that nobody present can describe, and in a selective pathway refusal is never free, because declining measurement carries a cost to selection. This paper argues that AI changes the stakes by converting measurements into inferences, and that inferences such as maturity status, injury flags and potential ratings are more consequential, less visible and less protected than the data they were drawn from. It separates measured, derived and inferred data, showing that inferred data is rarely shown to the athlete and travels furthest between organisations, and it removes the usual fallback by noting how readily small squads are re-identified from attributes that are already public. The remedy proposed is structural rather than a better consent form: collect only what a named coaching or safeguarding purpose requires, hold inferred data apart under restricted access, set retention periods that actually expire, and give every athlete measured before 18 a formal review at adulthood. Consent becomes a continuing process. The framework is untested in any pathway, and the practical obstacles to a review at 18 are conceded.