Mandatory age verification for pornography access: Why it can't and won't ‘save the children’

Age verification is gaining traction among western democracies as a proposed solution to restrict minors’ access to online pornography. In August 2021, Australia's eSafety Commissioner issued a call for evidence ‘seeking insights into effective age verification techniques’ to prevent people under the age of 18 from accessing sexually explicit materials (SEM) online (eSafety Commissioner, 2021). In doing so, Australia became the latest country to explore age verification for this purpose, following similar moves in Europe and the United Kingdom. The Australian Government's roadmap for mandatory age verification (the ‘AV Roadmap’) attracted significant rebuke from a range of stakeholders, particularly privacy activists. The roadmap formed part of the Government's response to a report entitled Protecting the Age of Innocence (Standing Committee on Social Policy and Legal Affairs, 2020), which followed on from a 2016 Inquiry into harm being done to Australian children through access to pornography on the internet (Senate Standing Committees on Environment and Communication, 2016). By August 2023, the eSafety Commission announced that the age assurance market was immature with significant gaps, citing feasibility and technical concerns, and recommending media literacy and education (2023a).

In this article we consider the ramifications of applying automated decision-making to the task of restricting pornographic access to minors. To do so, we draw together researchers from multiple disciplines, including media studies, cultural studies, education and computer science. While machine learning is regularly used to control access to SEM online, such as through pornography and nudity classifiers, media and education studies insist that the best way to support healthy sexual development in a digital context is through increased education to support sexual and media literacies. The article is therefore divided into two key parts: the could versus the should of age verification. In the first instance, we consider whether accurate and reliable age verification is actually possible. While there are numerous machine learning approaches to age verification, we focus on age estimation, analysing a dataset of 10,139 images processed through a commonly used state-of-the-art convolutional neural network (CNN) approach. We report on the accuracy of age estimation classifications via age bracket, gender and race, and examine the ways in which classifiers zone in on particular image segments and introduce discriminatory bias. In the second instance, we consider whether age verification is actually desirable. We discuss age verification in light of current debates on privacy, feasibility, pornography regulation and healthy sexual development.

We argue that the concept of employing automated processes to manage access to pornography is not only problematic but fundamentally misconceived. First, current state-of-the-art facial recognition technology is only accurate up to a certain level, and studies suggest that this is unlikely to improve. Existing age estimation software is not even close to being able to reliably distinguish between people who are under and over eighteen years old (the legal age for purchasing pornography in Australia). While it remains a common refrain in computer science that such systems simply require better training data, more sophisticated algorithms or other incremental improvement, our meta-analysis indicates that age estimation solutions from facial scans cannot ever be expected to achieve acceptable levels of accuracy. Second, the systems being proposed to automate age verification are unworkable. They pose onerous requirements on platforms of disparate sizes and scales and divert resourcing that could be spent on strategies that are proven to support healthy sexual development. Even if they could be made to work, and even if they were not open to circumvention and gaming by users, they require the gathering, storage and sharing of extensive personal data which is susceptible to serious privacy violations. Finally – and most profoundly – a mandatory age verification system will create barriers to post-pubescent young adults seeking information about sex online. Research shows that in order to support healthy sexual development we need to ensure that comprehensive, age-appropriate sex education is available to give young people the information about sex that they are interested in (including information about pleasure) (Kantor and Lindberg, 2020). It is not simply that age verification doesn’t support this aim; the problem is that age verification actually works against this outcome. The underlying problem with age verification, therefore, is not a technical but a political one: even if the system can be made to work, it should not.

Methods

While much previous work has revealed the inadequacy of age estimation algorithms through the lens of performance measurement (% accuracy, precision vs recall), less work has touched on exactly why these systems fail. Much of this is due to the epistemology of machine learning in this present moment which has tended towards focussing on algorithmic performance as the primary marker of quality, rather than how or why algorithms perform in a specific manner (Birhane et al., 2022). Thanks to a recent upswing in interest on explainability, however, there is a new focus upon unpacking the internal logics of machine learning systems for the purposes of algorithmic accountability. In this article, we focus specifically on biometric age estimation software as an approach considered by the eSafety Commissioner and explore their reliability and potential impacts by race and gender. Age estimation algorithms constitute any automated methods for determining the age of an individual. Such algorithms are prominently implemented as a sub-task of facial image analysis and have been studied extensively in machine learning research (Han et al., 2013). We consider CNNs, as a form of automated age estimation that is currently the subject of much interest, and as such would be likely candidates for consideration in any technical implementation of age verification. CNNs are machine learning solutions designed predominantly for classification problems, that are trained using a corpus of labelled data from a similar context to their intended domain of application.

In our analysis, we examine data sourced from four distinct age groups relevant to debates about access to pornography: ages 0–12, 13–18, 19–25 and 26 + . We choose these groups for several reasons. Because regulators are concerned with under 18 access to pornography, and privacy activists are concerned about users over 18 being incorrectly blocked, we focus on groups on either side of this 18yo threshold. However, because the literature on healthy sexual development differentiates between the impacts of pornography on pre-pubescent and post-pubescent young people, we examine these groups separately (0–12yo, 13–18yo). Although puberty commences at different ages, we take 12yo as the average age of puberty onset in Australia. Further, ‘young people’ up to age 25 are a demographic who also have specific needs in relation to sexual development and education. We additionally explore each of these categories by race and gender, to identify the potential for compounding disadvantages and harms.

“Deep Expectation” method

We classified the UTKFace Dataset using the pre-trained Deep EXpectation (DEX) CNN of Rothe et al. (2015). Since its introduction, the DEX CNN has contributed to contemporary solutions to age estimation, achieving “state-of-the-art results” (Agbo-Ajala and Viriri, 2021), as supported by the surveys of Sundararajan and Woodard (2018), and Hassan et al. (2021). Paired with the well-adopted IMDB-WIKI dataset, the DEX model has been cemented as a key development in age estimation research.

The authors understand that technologies similar to DEX were considered in Australia. The Protecting the Age of Innocence report, from which the AV roadmap derives, discussed facial analysis technologies as a potential approach (Standing Committee on Social Policy and Legal Affairs, 2020). Among possible forms of image data, biometric photo IDs issued by government bodies were frequently mentioned as possible inputs: Australian Driver's Licenses, Proof of Age Cards, and Australian Passports all prescribe centrally positioning a subject's head and shoulder-line within a margin of an almost square-ratio boundary (Queensland Police Services, 2023) (Australian Government Department of Foreign Affairs And Trade, 2023). This matches the specification of the IMDB-WIKI training data used for the DEX model, with only the differences of restricted facial expressions, hair, and accessories – although the DEX model nonetheless anticipates cases that enforce these restrictions.

The Inquiry also heard from the Head of Age Verification at the British Board of Film Classification (BBFC) about their digital Age Verification Certificate (AVC); one of their providers for administering AVCs, Yoti Australia Pty Limited (2019), reported on the feasibility of implementing age verification in Australia. The Yoti Limited (2022) ‘age scan’ technology uses a neural network approach, positioning it within the same class of solutions as the DEX model, although with claims of stronger statistical accuracy for younger age groups. Despite this, they recognised increased error rates for older individuals, already introducing potentialities for ageism, as previously articulated by Stypińska (2021).

To this end, the technologies considered for the AV roadmap align with the class of solutions containing the DEX model, and image data similar to the data evaluated within this article. Here we apply the DEX CNN as implemented in the Keras Python deep learning API (Uchida, 2022), and trained using the IMDB-WIKI dataset (again provided by Rothe et al. (2015)). It works by analysing an image of a subject's face via an ensemble of 20 sub-CNNs to predict their age, within the range of 0–100 years old. As we are interested in the inferences made in estimating age, we focus specifically on this and disregard the DEX method's gender inferencing capabilities, which falls outside the scope of our research question.

The DEX method provides an inference as to a subject's age as a vector of 101 values, where these indexes (ranging between 0–100) correspond directly to the probability that the subject's age is said index. The predicted age is then determined as the dot product of this vector, with the discrete years for each age as a class given by the formula:

E ( O ) = ∑ i = 0 100 Y i O i

Equation 1: Softmax expected value of age (Rothe et al., 2015)

O = { 0 , … , 100 } represents softmax output probabilities O i ∈ O produced by the model. This is multiplied by Y i ∈ Y corresponding to estimable years of age, which then determines the age estimation .

Facial image data pre-processing

Accurately testing the reliability of the age estimation model required ensuring that all facial images were consistently aligned. The DEX method pre-processes sample images by alignment, centring, and cropping (Rothe et al., 2015, 2.1). Alignments are achieved by rotation; we produced similar results by applying Gaspar and Garrod's method (2021), which not only rotates, but warps images to correctly display obscured faces. All images were required to centre the subject's face to include a 40% margin. For pre-processing, we applied the “DLIB” machine learning library (King, 2009), which detects the facial landmarks within images, cropping accordingly. A total of 9645 images from our corpus succeeded in all stages of the pre-processing, and those that failed this stage were removed from the input corpus Figure 1.

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Figure 1.

Example pre-process of image for age estimation.

Facial landmark detection and LIME explainability

After pre-processing, DLIB was further used to determine image segments that describe the subject's facial features meaningfully and consistently. This step allows us to later isolate which specific facial features the model is focusing on to estimate the subject's age Figure 2.

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Figure 2.

Facial landmark connotations. Note: The indices of the coordinates displayed in the diagram above correspond directly to those applied within the DLIB detection package, and are used to derive the following connotations: OBT (Outer Boundary Top); OBL (Outer Boundary Left); OBB (Outer Boundary Bottom); OBR (Outer Boundary Right); LEA (Left Eye Area); LE (Left Eye); LC (Left Cheek); N (Nose); UL (Upper Lip); M (Mouth); C (Chin); REA (Right Eye Area); RE (Right Eye); RC (Right Cheek). The area highlighted in light blue is also denoted as the F (Face) segment.

In addition to facial landmark detection, we also implemented the image module of the LIME explainability package (Ribeiro et al., 2016). LIME's image module creates a Boolean mask to reveal which specific image pixels detract from, or contribute to, the outcomes of the DEX model's inferences. This mask reveals the degree by which specific segments of an image contribute to its age estimation. Combining the facial landmark detection and LIME Boolean mask allowed us to further obtain the degree (as a percentage) by which specific facial segments contribute to the DEX model's inferences relating to the subject's age. An example is given in Figure 3.

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Figure 3.

Intersection of image segment defined by facial landmarks and image explanation. Note: The Boolean matrix is here conveyed by super-pixels of varying colour displayed in the LIME image explanation. Green super-pixels denote the value of ‘1’, whereas red super-pixels denote the value of ‘0’.

Finally, to summarise overall patterns of LIME image segment activation across the dataset of facial images, we summate the matrices of the corresponding Boolean masks in order to generate heatmaps, as are visualised in our results Figure 4.

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Figure 4.

Aggregation of LIME image explanations as Boolean masks to generate heatmap of image segment activations.

Findings: age estimation accuracy

We found significant inconsistencies due to race in the DEX model's classifications, with higher classification accuracy for the ‘Caucasian’ category, and the lowest quality for the ‘African’ category. We also found that young males are more likely to be misclassified than young females, particularly in the 0–12 age bracket.

Figure 5 reveals the overall spread of confidence in the output classification for each age bracket, i.e., how accurate is the model across each category and age bracket. Figure 6 shows the frequencies of differences between estimated and ‘true’ ages, i.e., how far the model's estimates are from the ‘true’ ages of input subjects. In Figure 6 a positive value would indicate that the model output has classified the subject as older than their true age. In both figures, the common ‘overlap’ (i.e., the subsample shared by all distinct distributions) is discounted from all distinct distributions, which in turn contrasts relevant differences between them.

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Figure 5.

Age estimation accuracy from the DEX model.

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Figure 6.

Distributions of age estimation difference (model estimate – true value).

With respect to race, wider confidence intervals (flatter and shifted further to the left) for younger age brackets indicate reduced accuracy for younger subjects (with ages 0–18 most affected). Notably, the ‘Caucasian’ category in the 0–12 age range performs best within this sub-group, while the ‘Asian’ category performs poorest. As shown in Figure 6.a, subjects of all races within younger age ranges (0–18) are estimated as significantly older than their true ages, by up to 45 years. Alternatively, for the 26–100 age range, the majority of races stabilise to a misclassification error margin of ±20 years; this excludes the ‘African’ category, which observes frequent misclassification for subjects appearing younger, by sometimes as much as 40 years. In fact, the ‘African’ category demonstrates the worst age estimation accuracy in all applicable cases.

In line with past studies of racial bias in facial classification, the ‘Caucasian’ category ranks best, peaking above 90% accuracy for all age ranges. Where statistical significance is established, the ‘Asian’ and ‘Other’ racial categories demonstrate less accurate yet similar characteristics. Also, the Indian racial category frequently demonstrates the narrowest confidence intervals, which indicates the greatest certainty in deviation of age estimations.

With respect to gender, we observe in Figure 5.b that the model has higher accuracies in age estimation results for the 0–12, 13–18, and 19–25 age ranges of female subjects, with male subjects receiving better accuracy in the 26–100 age range. Somewhat consistent with the findings from racial categories, there is less certainty in age estimations for subjects within the 0–12 and 26–100 age ranges. As shown in Figure 6.b, in the 0–12 age range, male subjects are slightly more likely to be misclassified as being older than their true age. Whereas in the 26–100 age range, female subjects are more likely to be misclassified as younger.

Findings: image segment activations for age estimations

Moving beyond classification accuracy, we also examined how facial segments contributed to age estimations, and how this interacted with racial categorisations. A claimed innovation of the DEX model is that the model does not rely on explicit facial landmarks to infer age (avoiding potential discriminatory bias). However, using LIME explainability modules, we determined that the classifier consistently relies upon facial landmarks to undertake age estimations, irrespective of whether the result is right or wrong. This is substantiated by the consistent activation of facial segments (F segments) by the DEX model, as noted across all age ranges and races. One such example is visualised for the ‘Caucasian’ category of ages 0–12 in Figure 7.

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Figure 7.

Heatmap of image segment activations for ‘Caucasian’ racial category.

The DEX model's reliance on facial segments is most apparent within the 0–12 age range. Specifically, strong activations are observed for Left Eye Area (LEA) and Right Eye Area (REA) segments across race categories performing successful classifications. Thereafter in the 13–18 age range, this reliance weakens as other segments begin to influence results. The ‘Caucasian’ and ‘Other’ categories both observe stronger activation in Outer Boundary Top (OBT) and Outer Boundary Bottom (OBB) segments, and this is pronounced in the 19–25 age range, which demonstrates further activation in the Outer Boundary Left (OBL) and Outer Boundary Right (OBR) segments. In the 26–100 age range, the Face (F) segment is least relied upon to determine age classifications. Furthermore, there is significantly more activation of the OBT segment than anywhere else.

Concerning racial categories, the trend continues with ‘Caucasian’ and ‘Other’ categories. For the ‘Caucasian’ category, the DEX model relies significantly on F segments. This reliance weakens in the 13–18 age range, where the F segment contributes to both accurate and inaccurate classifications. As a result, the OBB segment overtakes it as most contributing to accurate classifications. In the 19–25 age range, the F segment is much less related to classifications of any kind; rather in this age range, the OBL and OBB are influential upon accurate classifications. This finally adjusts to the OBR, OBL, and OBB segments within the 26–100 age range, where the F segment is least influential.

A visualisation of the image segment activations for accurate age estimations of both the ‘Caucasian’ and ‘Other’ racial categories (as aggregated for all subjects within each group) are provided in Figure 8.

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Figure 8.

Heatmaps of image segment activations that contribute to accurate age estimations.

For all other racial categories, statistical significance cannot be established for the age ranges of 13–25. Irrespective of this, the age ranges of 0–12 and 26–100 coincide with the trend that older subject age results in more inaccurate determinations of age. Except for the `African’ racial category, for which the DEX model does not rely primarily upon facial landmarks for all age ranges. The complete set of results that correspond to the image segment activations for age estimations are detailed in Appendix C.

Discussion: racial bias and facial landmarks

Our analysis indicates that using automated decision-making to estimate age is fraught for several reasons. The most prominent finding consistently reinforced in our analysis has been the frequent failure among the ‘African’ racial category. This is demonstrated in estimation accuracies, where said subjects scored consistently worse than other racial categories. While statistical significance could not be established for all age ranges, subjects aged 26 and over were generally misclassified as younger, sometimes by as much as 40 years. We perceive that the failure is due to the model's inability to generalise progressions of age in ‘African’ faces; supported by image segment activations, which exhibited high noise and trended to right and bottom outer boundaries of the image.

Whereas `Indian’ and `Asian’ racial categories had more stable age estimations, the `Caucasian’ racial category generally performed better. The `Other’ racial category outperformed all alternatives in age estimation accuracies. This racial category aggregates subjects including but not limited to Hispanic, Latino, and Middle Eastern descent. We interpret that the improved performance may be due to the larger variations of facial features (as a result of aggregating differing races) present in the ‘Other’ training data, enabling it to generalise with greater insight.

In estimating age, we found that the DEX model's accuracy is highly variable. For example, for subjects of the 0–18 range, the performance is particularly weak; younger subjects have been generally estimated as older, in some cases up to 45 years, while subjects aged 26 and over produce error margins of up to ± 20 years. In terms of gender, trends were only slight, and again contextualised by age range: within the 0–25 age range, female subjects had their ages more accurately estimated than males - the reverse was then observed within the 26 and over age range, where ‘males’ had more accurate estimations than ‘females’.

A long-standing feature of ML-based age estimation technologies has been the reliance upon facial landmarks to determine a person's age. The DEX model is introduced as an alternative ‘facial landmark agnostic’ approach. However, our analysis reveals that despite these claims, DEX still relied upon facial landmarks. We speculate that this may be a result of emergent model complexity, where the model independently anticipates and forms dependencies on specific facial features it perceives to be beneficial to age estimation. We found that the model frequently observes the subject's left and right cheeks, mouth, and chin image segments to infer its predictions.

Overall, our results convey that facial segments of an image consistently affect the DEX model's determinations. This is true for both successful and unsuccessful age estimations, implying that there exist instances where it is not possible to ascertain the subject's age from their face. These results beg the more fundamental question: is it correct to assume that an image of a subject's face can be used to accurately determine their age? Given that facial features – such as brow shape or lip curvature – can be visually inhibited, ethnically variant, cosmetically adjusted, or not necessarily correspond to the subject's age, it is incorrect to assume that an image of a subject's face can be used to accurately determine their age.

Supporting healthy sexual development

We must always remember that what can be described as an “error” in classification is, in each case, a human being attempting to access representations of sexuality. The implications of mistakenly refusing access to that material can be significant for each of those humans, as well as being a notable error in a CNN approach. The various disciplinary approaches represented by the authors show us that not only is accurate age estimation impractical – more than this; it is undesirable. To put the point at its simplest: from the perspective of sex education, the issue of how to ensure that young people aren’t harmed by unwanted encounters with pornography is best managed not by restricted access, but by education. Even if used as a preliminary screening tool alongside human oversight, age estimation software still presents significant privacy concerns and diverts resources that could be invested in evidence-based solutions.

Sex education research insists that the best approach to supporting healthy sexual development for young people is to ‘talk soon, talk often’ with them about sex (Walsh, 2011). This includes talking to them about pornography. Pornography is not a monolithic, homogenous category. Particularly in an age of digital production, pornography includes multiple genres, philosophical approaches and content that is generally more diverse than mainstream media (Comella and Tarrant, 2015; Taormino et al., 2013). Like other forms of media, pornography spans the gamut of genres from documentary to comedy, horror and romance. The eSafety Commission's approach presumes that pornography is ‘violent and extreme’, despite research demonstrating that ‘violence’ in pornography is often measured in ways that conflates violence with consensual kink (McKee, 2015). It further makes assumptions about what ‘real’ and ‘fake’ bodies look like, even though the construction of reality and authenticity are highly blurred in any curated media production, including pornography (Stardust, 2019) and follows outdated narratives about objectification that fail to recognise the difference between sex and sexism (Paasonen et al., 2020).

This approach is also problematic because it ignores the research on how people actually use pornography (Byron et al., 2021). Research on young adults’ consumption of pornography highlights that they are media literate, critical consumers, and can distinguish between real and simulated content (Buckingham et al., 2005). Many young people view pornography as a lower-risk alternative to real-life experimentation with sex (Smith et al., 2014). Sexual and explicit media can also play a role in affirming desires, bodies and practices of young people, particularly sexual minorities who are underrepresented in traditional media (Litsou et al., 2020). Explicit content can be “a source of knowledge, a resource for intimate practices, a site for identity construction, and an occasion for performing gender and sexuality” for young adults (Attwood, 2005, p.65).

One domain of healthy sexual development is open communication with trusted adults about sex (McKee et al., 2010). Taking an educational rather than censorious approach limits the risk that young people will be upset if they accidentally stumble across SEM, where the research shows that “blurry notions of harm [from pornography, notions that they hear from the media] bother young people more than the actual pornographic content they encounter” (see Spišák, 2016). As Spišák demonstrates, if parents get angry or upset at young people when they stumble across SEM, that is more damaging than the sight of the material itself. Parents therefore need to understand their own sexual values – for example, if they disapprove of sex outside of long-term, committed, loving relationships – and be having conversations with young people about how their values differ from the representations encountered in pornography. Open communication limits the risk of harm – but perversely, censorship regimes contribute to harm by making it clear to young people that they should be embarrassed if they do stumble across material, and should keep it a secret. Green et al.'s work on “national contexts for the risk of harm being done to children by access to online sexual content” demonstrates that not only does the meaning of “harm” differ in different cultural contexts, but also that young people think about “harm” in very different ways from their parents (Green et al., 2020).

In this context, the ideas of ‘protection’ and ‘innocence’ are at least misleading, and deliberately obfuscatory. The ‘innocent’ children referred to in the Protecting the Age of Innocence include sixteen and seventeen-year-old people who, in most states and territories, can legally consent to sex. A historical consideration of the language of ‘protection’ in governmental legislation shows that it has commonly been used to sugar coat legislation that controlled groups of people – women, children and Indigenous peoples (see Baron, 1981; Egan and Hawkes, 2009; Nettelbeck, 2012). There is something particularly perverse in using the language of protecting people from themselves. Post-pubescent adolescents and young adults want to know more about sex. Restricting access aims to protect them from their own – common and healthy – interest in learning more about sex.

And still, restricted access systems are unlikely to stop young people from accessing SEM: UK research suggests that teenagers can easily avoid age verification (Yar, 2019) and may get around age checks using the dark web or by using third-party technologies to modify their images, impersonate others or assume a false identity, putting them at greater risk of encountering child abuse images (Blake 2018). Restricting access only contributes to a culture of shame about sex that is distinctly unhealthy (Wilson et al., 2012). We must not ‘protect’ young adults from learning about sex. Rather, we must support them to do so in healthy ways. In the eSafety Commission's own research, young people report wanting agency over whether, when and where they view online pornography, and “expressed their right to safe, autonomous sexual development and exploration” (2023b, 6). Young people were concerned that age assurance was of limited efficacy and had privacy and security issues, and viewed education (including porn literacy and sex/relationship education) as the most helpful tool (2023c).

Conclusion

Our analysis demonstrates that automated age estimation systems cannot be made to work in a reliable way that ensures accuracy, reliability or fairness. Machine learning approaches replicate human age estimation practices that are already problematic and biologically determinist. They rely on stereotypes about how highly divergent physical characteristics – hair, wrinkles, jawlines. Even when machine learning models are sold on a promise of being debiased and agnostic to specific facial features, our analysis shows that they continue to attend to specific physical characteristics in estimating age. The public datasets used to train and test these models themselves presume the truth of publicly available metadata as it is organised into rudimentary categories of race and binary categories of gender. In our analysis, a popular facial data set, albeit one which reproduces problematically limited demographic characterisations, revealed how a leading CNN skewed towards accuracy in ‘Caucasian’ faces. While classifiers can be tweaked to produce more true positives, it is a zero-sum game as doing so will inevitably increase the incidence of false negatives, and vice-versa. Because it is not possible to design an ML age classifier that affects all populations identically, software engineers are required to make decisions about what constitutes an acceptable rate of collateral damage – decisions that have significant trade-offs and implications.

However, the lack of reliability is not our fundamental disagreement with these technologies. Even if age estimation systems could be made to work, they should not be. This is a case study where automated decision-making is being misapplied to try to solve a social, cultural and educational problem rather than a technical one. Machine learning is often seen by governments as a panacea for complex policy areas. Instead of solving problems, machine learning systems often introduce further problems and distract from existing evidence-based policy options. When we say the problem is an educational one, we refer not only to comprehensive sex education and media literacy for adolescents. Rather, the hubris around age verification and the financial investment into making it feasible speaks to the need for porn literacy education among adults, including politicians. Porn literacy is developing as an important concept in research, deriving from earlier work on medial literacy (Smythe, 2018). Recent work has begun to think about “reading porn well” rather than taking an abstinence-based approach (Byron et al., 2021).

If our commitment is to supporting healthy sexual development in a digital context, focusing on automated processes is a distraction from the positive, practical, evidence-based steps that would make a difference. Relying on a rigid threshold of whether a user is under/over 18 erases fundamental differences between pre-pubescent children and post-pubescent adolescents, two groups that have very different needs and relationships to sex. Given that the biggest risk factor for pre-pubescents is the use of pornographic materials in the commission of sexual assault by adults on children (Langevin and Curnoe, 2004), governments must invest in community-led prevention and ensure that child protection services are funded at an appropriate level. Public health messaging must make it clear that the vast majority of child sexual abuse is perpetrated by someone already known to the victim, including close family members, and other authority figures such as teachers, coaches or priests (YWCA, 2017). On this point, governments must remove barriers to reporting such as the so-called ‘Seal of the Confessional’ which has been used by Catholic priests to conceal child sexual abuse. In terms of accidental exposure, the existence of parental familiarity with and use of ‘walled garden’ environments where pre-pubescents can safely play online is vitally important. As noted above, research shows that when young people accidentally stumble across forbidden material online, the biggest harm they face is fear of their parent/guardian's anger because the young person has (they worry) done the wrong thing (Spišák, 2016). Therefore, open communication between prepubescents and parents/guardians about sex is at the root of supporting healthy sexual development (Walsh, 2011).

By comparison, post-pubescents require legislated comprehensive, developmentally-appropriate sex education with a focus on providing the information they actually want about sex (Fine and McClelland, 2006). Young people consistently report they want to know more about pleasurable aspects of sex (Fisher et al., 2019, pp. 80–81). A comprehensive curriculum would teach students about pleasure as well as gender diversity and non-monogamy, in addition to human rights that have a significant effect upon health and wellbeing. LGBTIQA + health relies upon ‘the promotion of progressive sexuality education messages in classrooms addressing homophobia, sexual autonomy, sexual experimentation’ (Jones and Hillier, 2012). Given that a domain of healthy sexual development is ‘competence in mediated sexuality’ (McKee et al., 2010), it is also vitally important that young people receive appropriate training in porn literacy – knowing how to read porn well and make porn well (for example, selfies) (Byron et al., 2021). Sex education should also include sex-positive versions of porn literacy, developed with input from experts including sex educators, young people and pornography performers and producers (Stardust, 2016).

In practice, a restricted access scheme could include a simple yes/no question that asks users whether they are of age to consent in the jurisdiction in which they are accessing the content. Such an approach would serve the purpose of minimising prepubescent children, pubescent adolescents or young adults who do not know about the existence of SEM, from accidently accessing them. It also signals clearly to the consumer that a decision about consent to view material must be made, ensuring they will not ‘stumble’ across or be spammed by material they don’t want to see (Egan and Hawkes, 2009). But instead of opting for such an approach, governments are increasing their regulatory burdens by scaling up their investments in automation and data collection. This ought to raise suspicion. Instead of investing in consent education or porn literacy, significant resources, energy and time are being diverted towards fuelling biometric surveillance technologies – including nudity filters, pornography classifiers and CSAM detectors – which continue to raise serious privacy, accuracy and accountability issues. If machine learning is being pitched as the magic solution to human problems, we ought to pay greater attention to how the ‘problems’ are being articulated in the first place.

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