Position: Age Estimation Models Do Not Process Biometric Data

TL;DR

This paper empirically demonstrates that age estimation neural networks do not process biometric data in a way that enables individual identification, highlighting regulatory and transparency concerns.

Contribution

It provides empirical evidence that age estimation models do not generate identity-discriminative representations, informing regulatory discussions and transparency efforts.

Findings

14 models evaluated across 3 benchmarks show no identification capability.

Age estimators fall far below identification thresholds.

Models cannot identify individuals based on biometric data.

Abstract

When a neural network estimates someone's age from a photograph, does it process biometric data? The answer depends on whether identity-discriminative representations arise within the network during inference, a question that may seem trivial to ML researchers but triggers consent requirements under GDPR, statutory damages under BIPA, or high-risk AI classification under the EU AI Act. Yet no regulatory guidance addresses it. This position paper provides empirical evidence: 14 models evaluated across 3 face verification benchmarks show age estimators fall orders of magnitude short of identification thresholds. Age estimation models cannot identify individuals. We call on researchers to provide transparency about what systems store and can do, and on regulators to distinguish transient processing from template storage.