The Folly of AI for Age Verification

2025-05-27

AI Summary

This paper examines the feasibility and fairness of AI-driven age verification, highlighting critical security and equity concerns. Method: Employing a cross-layer attribution framework, the study integrates empirical bias evidence from facial recognition and remote proctoring systems, intrinsic limitations of AI models, and physical constraints of sensing hardware to systematically identify irreducible sources of bias at both technical and hardware levels. Contribution/Results: The analysis demonstrates that AI-based age verification is readily circumvented and exhibits systematic misclassification—particularly against racial minorities and individuals from lower socioeconomic backgrounds—with error rates substantially exceeding those of government-issued ID verification. Consequently, the technology is deemed neither secure nor equitable under current and foreseeable conditions, rendering its deployment irrational. The paper concludes with a policy recommendation: regulatory authorities should withhold approval for real-world implementation of AI-driven age verification systems.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy
• Humans and AI: AI for Accessibility

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In the near future a governmental body will be asked to allow companies to use AI for age verification. If they allow it the resulting system will both be easily circumvented and disproportionately misclassify minorities and low socioeconomic status users. This is predictable by showing that other very similar systems (facial recognition and remote proctoring software) have similar issues despite years of efforts to mitigate their biases. These biases are due to technical limitations both of the AI models themselves and the physical hardware they are running on that will be difficult to overcome below the cost of government ID-based age verification. Thus in, the near future, deploying an AI system for age verification is folly.

Problem

• AI age verification systems are easily circumvented
• AI misclassifies minorities and low socioeconomic users
• Technical biases persist despite mitigation efforts

Innovation

• AI models for age verification
• Facial recognition technology
• Remote proctoring software