The Folly of AI for Age Verification

Reid McIlroy-Young

REVIEW 4 major objections 6 minor 25 references

The Folly of AI for Age Verification

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that allowing AI to verify ages online would produce systems that are easy to circumvent and that disproportionately misclassify minorities and lower-income users, making AI age verification folly.

desk verdict A timely policy essay using known fairness results, but the categorical 'will' overstates what the evidence shows. read the letter →

arxiv 2506.00038 v1 pith:YWDCDU6D submitted 2025-05-27 cs.CY cs.AI

Reid McIlroy-Young This is my paper

classification cs.CYcs.AI

keywords ageverificationcomputervisionalgorithmicbiasfacialrecognitionremoteproctoringmodeldriftwebcamspoofingAIpolicy

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper's claim is that if a government lets companies use AI to verify website users' ages, the resulting systems will be both easily circumvented and disproportionately wrong for minorities and low-income users. The argument is predictive, not experimental: it shows that closely related face-analysis systems—gender classification and remote proctoring—still carry these flaws after years of mitigation, and argues that age-verification models use the same underlying technology. The paper also catalogs concrete failure modes: webcam spoofing via simulated USB cameras or printed images and videos, poor handling of low-quality images that punishes users with bad hardware, training-data bias, and drift as appearances change. Its conclusion is that fixing these problems would cost more than simply checking government IDs, so permitting AI age verification in the near term is folly.

What carries the argument

The carrying object is the convolutional neural network (CNN) pipeline that takes a face photo, locates the face, and predicts an age label—the same architecture used in face classification and remote proctoring. The paper treats this pipeline as a black box and maps known failure modes onto it through a taxonomy of four error sources: image spoofing, low-quality images, systematic demographic bias, and model drift. That mapping is the machinery of the argument: rather than testing age-verification software directly, it transfers documented failures from neighboring tasks to the age-verification task. The taxonomy also distinguishes false positives (underage users wrongly admitted) from false negatives (adult users wrongly blocked), which lets the paper show that both users and website operators bear harms, and that different technical causes lead to different error types.

What would settle it

A direct audit of a commercial age-verification API on a balanced photo set labeled by age, sex, and skin tone, plus a USB virtual-camera spoof test, would settle the claim: near-zero demographic error differences and near-zero spoof success would contradict the paper's prediction.

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Extended reading notes

Core claim

The central claim the paper tries to establish is that age-verification AI, if permitted by regulators, will predictably fail on two fronts at once. It will be easy for determined underage users to defeat, because webcam-based deployments lack cryptographic verification and can be spoofed with virtual USB cameras, printed images, videos, or 3D masks. It will also be inequitable, because commercial face-analysis systems already underperform systematically on darker-skinned users and in low-quality conditions, and age-verification models share the same convolutional-neural-network pipeline and camera hardware. The paper claims these failures are structural rather than incidental: they follow from the cost structure of training data, the physical limitations of commodity webcams, and the fact that models degrade as real-world appearances drift. Therefore, the author concludes, the economically rational path is government-ID-based verification, and permitting AI age verification is folly.

Load-bearing premise

The paper's equity claim depends on the assumption that age-estimation models will inherit the demographic biases documented in gender classification and remote proctoring; it presents no direct error-rate data for age-verification systems themselves.

Editorial extensions

If this is right

Regulators should not accept AI-only age checks without audited demographic parity and spoof-resistance testing, because the paper predicts both will fail in practice.
Age-gating mandates that permit 'commercially reasonable methods' would, if AI is used, shift costs onto low-SES users who lack high-quality cameras and lighting.
Webcam-based verification without cryptographic attestation will let underage users pass as adults, so the legal age gate would not actually block the target group.
Because models drift as hairstyles and appearances change, any deployed system would require continuous retraining; the paper implies this maintenance burden makes ID-based verification the cheaper option.
Government ID verification becomes the default safe option; AI systems would not merely be imperfect but more expensive to fix than the alternative.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper leaves implicit a concrete audit design: measuring an age-verification API's error rates across skin tone, age, and camera quality would directly test its prediction, and the same method could be applied before any government approves such systems.
If the analogy holds, then every new face-based task (liveness detection, emotion recognition, deepfake screening) that shares the CNN backbone should be assumed biased until shown otherwise; regulators could require a demographic-accuracy audit as a precondition for any such deployment.
The spoofing analysis suggests an arms race: adding cryptographic camera attestation would close USB spoofing but would exclude low-end and desktop hardware, which would likely deepen the socioeconomic disparity the paper identifies.
A further implication is that age thresholds amplify bias: if errors are not symmetric around the threshold, a small systematic over-estimate of age could let a cohort of minors through, while a small under-estimate would block many adults; the paper does not quantify this threshold effect.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Plainly: this is a policy essay, not an empirical paper. It argues that if governments permit AI-based age verification, the systems will be both easily spoofed and biased against minorities and low-income users. The spoofing claim is well supported. The bias claim is plausible but stated with more certainty than the evidence allows.

What's new is the packaging, not the contents. The paper restates known fairness results from Gender Shades and remote proctoring and applies them to age verification. That framing is modestly useful: it brings together two distinct failure modes (circumvention and disparate error) into a single taxonomy, and it explicitly distinguishes false positives (user harmed) from false negatives (operator harmed). The writing is clear and the policy timing is good.

The soft spots are real. The headline 'will disproportionately misclassify' is a categorical prediction. The support is analogical: gender classification bias and proctoring bias. Age estimation is a regression task with noisy, threshold-dependent labels; its training sets differ from gender corpora. No direct error-rate data for age verification systems, broken out by demographic or image quality, are presented. The paper's own taxonomy separates face detection from age prediction, so it is unclear where the predicted bias is supposed to enter. That overstatement matters because the policy conclusion rests on it. The cost claim—that fixing AI systems will cost more than government ID-based verification—is asserted without any price comparison. And the phone-based secure verification route is mentioned in passing but not explored, even though it could change the policy calculus.

Who should read it: regulators, advocates, and journalists who want a readable map of failure modes. Researchers will not find new results. It deserves a serious referee because the topic is important and the synthesis is honestly done, but the categorical language needs to be scaled back to 'likely' or 'may,' and the age-estimation bias literature should be engaged directly or acknowledged as thin. I would send it to review, with major revision expected.

Referee Report

4 major / 6 minor

Summary. The paper argues that if a governmental body permits AI-based age verification for online age gating, the resulting system will be both easily circumvented and disproportionately misclassify minorities and low socioeconomic status users. The argument is analogical: it draws on documented bias in commercial gender classification (Gender Shades) and in remote proctoring software, and on known limitations of computer vision systems (low-quality inputs, model drift), to predict that age-estimation models will inherit these problems. The paper proposes a taxonomy of error sources and concludes that because these biases are technically difficult to eliminate below the cost of government ID-based verification, deploying AI for age verification is 'folly.' The paper is a position/argument piece with no original experiments or data.

Significance. If the paper's predictions are correct, the result would be policy-relevant: it would caution regulators against accepting AI-based age checks without strong auditing and would support the continuing use of government ID-based verification. The paper's main strength is its clear, honest survey of known failure modes in adjacent facial-analysis tasks and its explicit identification of spoofing and demographic bias as likely risks. However, the absence of age-estimation-specific error data and the internal inconsistencies in the error taxonomy mean the paper currently functions as a plausible risk argument rather than a demonstrated prediction.

major comments (4)

[Abstract and Model Errors] The central claim that AI age verification 'will disproportionately misclassify minorities and low socioeconomic status users' is supported only by analogy with gender classification (Buolamwini and Gebru 2018) and remote proctoring (Burgess et al. 2022). No direct error-rate data for age-estimation models, by demographic group or image quality, are presented, and the analogy is not automatic: age estimation is a regression task with threshold-dependent labels, training corpora differ, and face-detection preprocessing can either amplify or attenuate bias. The categorical 'will' therefore overstates what the cited evidence supports. Please either provide direct evidence from age-verification systems or revise the claim to 'likely' or 'at risk of'.
[Table 1 and Image Spoofing] The taxonomy contradicts the paper's own definitions. The paper defines a false negative as the model incorrectly classifying an underage user as above age ('Image Spoofing', first paragraph), yet Table 1 assigns 'USB Webcam Spoofing' and 'Physical Image Spoofing' to 'false positive', and the text states 'This type of spoofing attack leads to false positives.' Conversely, Table 1 lists 'Low Quality Images' as a 'false negative' source, while the text says that an 'uncertain' (e.g., blurry/dark) image is likely to cause the developer to reject the user, which is a false positive under the paper's definition. Because the taxonomy is the paper's organizing framework, these swaps make it difficult to evaluate which harms are being claimed.
[Image Spoofing] The claim that the system 'will be easily circumvented' is qualified by the paper's own statement that 'software and hardware based spoofing are not possible on modern cellphone cameras as they do implement cryptographic verification systems.' Since a large share of age-gated access occurs on smartphones, the blanket statement in the abstract and conclusion needs a platform-dependent qualification or evidence about webcam-only deployment; otherwise the central 'easily circumvented' claim is overstated.
[Conclusion and Abstract] The claim that the necessary fixes 'will be difficult to overcome below the cost of government ID-based age verification' is asserted without supporting cost data or comparison. The paper provides no evidence on the cost of collecting balanced training data, hardware upgrades, or continuous re-training, nor on the cost, accessibility, and privacy implications of government ID-based verification (which excludes populations without IDs). This cost-superiority premise is load-bearing for the conclusion that AI-based age verification is 'folly' rather than merely immature. Please add a comparative analysis or limit the conclusion to 'AI-based age verification cannot currently be trusted for regulatory age gating.'

minor comments (6)

[Abstract] The phrase 'Thus in, the near future' is a typo; it should read 'Thus, in the near future,'.
[Application Context] The question-and-answer format (e.g., 'What does it do? There are many AI systems...') reads like an internal template and should be removed or rewritten for a formal paper.
[Remote Proctoring Systems] The word 'mat ´eriel' should be 'matériel' (or 'equipment') for readability.
[Application Context] The claim that 'most [AI age-verification systems] are used for internal tools' lacks a citation; if this is asserted as fact, please provide a source or mark it as speculation.
[References] The reference to Heilweil (2020) is a Vox news article; consider replacing it with a peer-reviewed source for the claim that remote proctoring systems have issues with darker-skinned individuals, or keep it but acknowledge that it is a journalistic source.
[Figure 1] The example output of a commercial age recognition system on a photo of Marvin Minsky is illustrative but unlabeled as to which system produced the output; if the image is from a specific product, a citation should be given.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning found: the argument is an analogical policy critique grounded in external studies, not a derivation that reduces to its own inputs.

full rationale

The paper makes no formal derivation and fits no parameters. Its central claim is that AI age-verification systems will be easily circumvented and will disproportionately misclassify minorities and low-socioeconomic-status users, because similar systems (facial recognition and remote proctoring) have shown such problems despite mitigation efforts. Every load-bearing piece of evidence is drawn from external sources: Gender Shades (Buolamwini and Gebru 2018), proctoring studies (Burgess et al. 2022; Pokorny et al. 2023), and general machine-learning bias surveys (Mehrabi et al. 2021). The author does not define age-verification performance in terms of those prior results, nor does the conclusion follow from the cited data by construction. The argument is analogical and therefore may be vulnerable to the objection that age estimation is not identical to gender classification or proctoring, but that is a question of evidential strength, not circularity. There are no self-citations that carry the argument, no fitted inputs relabeled as predictions, and no uniqueness theorem imported from the author's prior work. The paper is self-contained as a position piece: it asserts a prediction, supports it with external evidence, and does not reduce the prediction to its own assumptions or definitions. Thus the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central argument imports several unproven premises from prior work and from the author's framing. The most important is that bias observed in gender classification and remote proctoring transfers to age estimation, and that deployment will occur on insecure webcams. The cost comparison is asserted without data. No free parameters are fitted because the paper performs no quantitative analysis.

assumptions (4)

domain assumption Bias in facial recognition and remote proctoring transfers to age estimation.
The paper's main evidence is analogical; no age-estimation-specific fairness data are provided. The analogy is established in the 'Fairness Concerns in AI' sections.
domain assumption Deployed age verification will use generic web cameras without cryptographic verification.
The spoofing arguments assume this setup, but the paper notes cellphone cameras have cryptographic verification, which narrows the scope of the assumption. See the 'Image Spoofing' section.
ad hoc to paper The cost of mitigating bias and spoofing exceeds the cost of government ID-based verification.
Asserted in the abstract and conclusion without a cost model, quantitative comparison, or cited cost data.
domain assumption CNN-based models trained with backpropagation are representative of deployed age verification AI.
The Background section defines AI this way and excludes other approaches such as phone-based secure elements or hardware-backed liveness detection.

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Cite this review

Pith. "Pith review of The Folly of AI for Age Verification." pith.science (2026). https://pith.science/paper/YWDCDU6D

@misc{pith2026250600038,
  author       = {Pith},
  title        = {Pith review of: The Folly of AI for Age Verification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YWDCDU6D}},
  note         = {Machine review of arXiv:2506.00038}
}

read the original 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.

Figures

Figures reproduced from arXiv: 2506.00038 by the authors.

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Reference graph

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ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...
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" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

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Albiero, V.; Zhang, K.; King, M. C.; and Bowyer, K. W. 2021. Gendered differences in face recognition accuracy explained by hairstyles, makeup, and facial morphology. IEEE Transactions on Information Forensics and Security, 17: 127--137

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Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems, 29

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