Does existing legislation on CSEA/CSAM across the Five Eyes nations allow for criminal liability or any other form of accountability with regards to AI-generated CSAM?
Notice bibliographique
Résumé
Child sexual exploitation and abuse (CSEA) is considered a violation of children’s rights and dignity (Ngo, 2021). A ‘widespread, worldwide issue of concerning magnitude’ that affects both girls and boys (Simon, Luetzow & Conte, 2020: 2), CSEA may entail a series of negative effects for victims, which can impact their physical, mental or psychological health, their emotional wellbeing, social skills and interpersonal relationships, economic status, as well as vulnerability to future victimisation (Fisher et al., 2017). Within this, technology and related platforms or online environments are considered spaces which can be protective, but equally also raise the risk for children’s safety, increasing their vulnerability to be victimised in CSEA (Simon, Luetzow & Conte, 2020). This vulnerability to victimisation is, in fact, considered to be higher for children than adults (Quayle, 2016). The rapid development of technology has led to the birth of new, immersive forms of technology, which are usually grouped under the umbrella term ‘eXtended Reality’ (XR) (Huang, 2022). Prominent among these emerging technologies is Artificial Intelligence (AI), defined widely by Bahoo, Cucculelli and Qamar (2023: 1) as ‘the system's ability to interpret data and leverages computers and machines to enhance humans' decision-making, problem-solving capabilities, and technology-driven innovativeness’. As such and following the increasing dissemination of child sexual abuse material (CSAM) noticed across the clear and dark web, AI can prove to be a valuable tool in the efforts against CSEA by allowing the invention of detection intelligence algorithms that will use deep-learning technique as methods of accurate detection of CSAM online (Lee et al., 2020; Ngo, McKeever & Thorpe, 2023). However, on the downside, and mainly with regards to its content generative aspect, AI can also be misused by offenders to create CSAM with varying levels of realism that can often be hardly distinguishable from real-life material (Internet Watch Foundation, 2023). Irrespective of whether AI-created CSAM involves artificial children or children modelled after real-life children, there is widespread concern that it can be a pathway to higher levels of CSEA offending that may include the sexual exploitation and abuse of children in real life (Internet Watch Foundation, 2023). As such, it requires a robust and clear legislative response, particularly with regards to the issue of accountability over AI-created CSAM. This call comes amidst a hotly contested debate, with some stakeholders promoting notions that CSAM created via generative AI does not actually hurt real children or that it may also serve to divert potential offenders from sexually exploiting and abusing real children, and others who fear that generative AI-created CSAM may well be the first step on a pathway towards higher offending in CSEA with real children (Internet Watch Foundation, 2023). Based on the above, examining the existing legislative context of the Five Eyes countries, which comprise Australia, Canada, New Zealand, the United Kingdom (UK) and the United States of America (USA), becomes crucial in order to assess the readiness of their regulatory frameworks against phenomena of AI-created CSAM. These countries have been selected due to their democratic and open political systems, their high levels of technological advancement and literacy, as well as their progressive and advanced legislative systems, which often serve as the regulatory blueprints for other countries across the globe who often wish to model their legislation after them.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,013 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».