Скачать или смотреть AI Age Estimation from Selfies: Tech, Accuracy & Privacy Risks

Computer vision AI analyzes facial landmarks, wrinkles, skin texture, and expressions from single selfies to predict age with ~5-year mean absolute error on young adults—but drops sharply for older faces, females, and smiles. Used in age-gating, ads, and health screening, it amplifies human biases while raising surveillance concerns.
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How AI Age Estimation Works
Key Features Analyzed:
Landmarks: Eye distance, jawline shape
Texture: Wrinkles, pores, elasticity
Color: Skin tone uniformity
Expressions: Smile lines, asymmetry

Models: CNNs (e.g., DeepFace) trained on millions of labeled images. 2026 accuracy: 1.5-2 years MAE for 20-40s, 10+ years for 60+
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Biases & Accuracy Issues
AI mirrors/exaggerates human errors:
Age Bias: Overestimates young/smiling faces, underestimates elderly
Gender Bias: Less accurate for females (AI error vs. males)
Race Bias: Underperforms on non-Caucasian datasets
Result: 20-30% worse for marginalized groups
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Applications vs. Risks
Use Case Benefit Risk
Age-Gating Apps Blocks underage access False positives deny adults
Targeted Ads Relevant marketing Invasive profiling
In-Store Analytics Demographic insights Mass surveillance
Health Screening Biological age proxy Privacy breaches, bias in care
EU AI Act 2026: Mandates bias audits for high-risk facial apps
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