Lessons from France on the regulation of Internet pornography: How displacement effects, circumvention, and legislative scope may limit the efficacy of Article 23

Abstract

In 2020, the French Parliament passed an amendment that put the country at the forefront of attempts by democratic states to restrict young people's access to legal online pornography. This study examines the necessity for and potential efficacy of the amendment, Article 23, through a comparative analysis of emerging legislative and regulatory approaches in France, the UK, Canada, Utah, and Germany, and through a survey of French 15-, 16-, and 17-year-olds. Among other things, our survey shows that 41% of 15-, 16-, and 17-year-olds in France visit dedicated pornographic sites, on average monthly and often much more frequently. However, the range of media platforms via which French adolescents are exposed to pornography, their knowledge about technologies that could circumvent age verification, and the power, scope, and implementation of Article 23 may limit the legislation's efficacy. Our findings suggest the mechanisms that may limit its efficacy include media displacement, socio-technical circumvention, and the Article's relatively broad and imprecise nature. This study has implications for legislators and regulators in democratic countries beyond France as they too grapple with the challenges of regulating online pornography. Furthermore, it extends the often contradictory and/or limited evidence that exists about adolescents' consumption of pornography.

No evidence that Chinese playtime mandates reduced heavy gaming in one segment of the video games industry

“…This uneven compliance may plausibly lead to either a lack of reduction in heavy playtime within small game companies, or even an increase, as heavy players migrate from now-regulated ‘big’ games by ‘big’, compliant companies to non-regulated ‘small’ games by non-compliant ‘small’ companies. We have seen similar phenomena in internet pornography regulation, where restriction of access to minors in one domain resulted in their displacement to unregulated spaces 55 . Our Unity data chiefly consists of small company games, and such a displacement migration may appear to be consistent with the increased likelihood of heavy gaming postregulation that we observed.…”

“…Effective online regulation must overcome several key limitations. First, regulatory compliance must be consistently enforced among relevant corporate bodies in order to prevent displacement: the movement of individuals from compliant to uncompliant subsets of a sector 55 . For example, in Germany, the Interstate Treaty on the Protection of Minors in the Media requires pornographic websites to implement age verification.…”

“…Accurate and reliable identification of minors online is a challenging task 62 . The use of alternative identification documents and technological solutions provides a well-known avenue to regulatory escape in this domain 55 , 63 . Thus, despite attempts to age-restrict online access to products such as nicotine, gambling and pornography, their use by minors is common: in 2019, 11% of 11–16 year olds in the UK reported gambling during the prior week 64 .…”

No evidence that Chinese playtime mandates reduced heavy gaming in one segment of the video games industry

“…Accurate and reliable identification of minors online is a challenging task 62 . The use of alternative identification documents and technological solutions provides a well-known avenue to regulatory escape in this domain 55,63 . Thus, despite attempts to age-restrict online access to products such as nicotine, gambling and pornography, their use by minors is common: in 2019, 11% of 11-16 year olds in the UK reported gambling during the prior week 64 .…”

Transparency reporting on child sexual exploitation and abuse online

“…By their nature, these technologies are not as accurate and need to be trained on large datasets to improve their effectiveness (Ngo et al, 2022[51]). Thorn's Safer tool (Thorn, n.d. [56]), Google's Content Safety API (Google, n.d. [57]), and Meta's AI technology (Meta, 2018 [58]) are examples of technologies that use or incorporate classifiers and AI technology to detect previously unknown CSEA content. When used in conjunction with tools that detect known and previously 'hashed' CSEA where there is a likelihood of finding new patterns or matches for violative material, their effectiveness can be improved.…”