14个年龄估计模型实测表明，无法通过人脸图识别个人身份。
Position: Age Estimation Models Do Not Process Biometric Data
- 在3个验证基准上测试14个模型，评估其身份识别能力
- 年龄估计模型的识别精度远低于身份识别阈值，差几个数量级
- 呼吁研究透明化，监管应区分临时处理与模板存储
当神经网络从照片中估计年龄时，是否在处理生物特征数据？答案取决于推理过程中网络是否生成可区分身份的表征。这一问题对机器学习研究者看似简单，却可能触发GDPR的同意要求、BIPA的法定赔偿或欧盟《人工智能法案》中的高风险AI分类。然而目前尚无监管指引。本文通过实证研究：在3个面部验证基准上评估14个年龄估计模型，发现其识别能力相差数个数量级，远低于身份识别阈值。年龄估计模型无法实现个体识别。我们呼吁研究人员公开系统所存储与可执行的功能，并敦促监管机构区分临时处理与模板存储。
原文摘要 · Abstract (English)
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.
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