Child sexual abuse material (CSAM) is a serious and pervasive problem that affects millions of children and adults worldwide. CSAM is any visual depiction of a minor engaged in sexually explicit conduct, and it can have devastating and long-lasting consequences for the victims and their families. CSAM is also a lucrative and illegal industry that exploits the vulnerabilities of children and the demand of predators.
The advent of artificial intelligence (AI) has brought new opportunities and challenges for combating CSAM. On one hand, AI can be used to detect, report, and remove CSAM from online platforms, and to identify and rescue victims and prosecute offenders. On the other hand, AI can also be used to create, distribute, and consume CSAM, and to evade detection and enforcement.
A new report by researchers from the Stanford Internet Observatory and Thorn, a nonprofit organization that works to end child sex trafficking and abuse, reveals how generative machine learning (ML) models, which can create realistic images, videos, and texts, are exacerbating the problem of online sexual exploitation of children.
It report, titled “Generative ML and CSAM Implications and Mitigations”, examines the current state and future trends of generative ML models, and their potential impacts and risks for Child sexual abuse material (CSAM) production and consumption. The report also provides recommendations and best practices for mitigating the harm of generative ML models for CSAM, and calls for more collaboration and action from various stakeholders, such as the tech industry, the government, the civil society, and the research community.
Generative ML models can produce realistic and convincing CSAM that is indistinguishable from real CSAM, and that can cater to the preferences and fantasies of predators. These models can also generate novel and diverse CSAM that does not exist in the real world, and that can increase the demand and supply of CSAM.
Generative ML models can also create synthetic identities and personas that can be used to lure, groom, and exploit children online, and to deceive and manipulate adults. These models can also generate realistic and personalized texts and conversations that can mimic human interactions and emotions, and that can facilitate online sexual coercion and extortion.
Generative ML models can also enable the anonymization and obfuscation of CSAM producers and consumers, and the circumvention and evasion of detection and enforcement mechanisms. These models can also pose challenges and limitations for the verification and attribution of CSAM, and the identification and rescue of victims and the prosecution of offenders.
Developing and implementing ethical principles and guidelines for the development and use of generative ML models, and ensuring that they are aligned with the human rights and the best interests of children.
Enhancing and enforcing the legal and regulatory frameworks and standards for the prevention and prosecution of CSAM, and ensuring that they are consistent and harmonized across jurisdictions and platforms.Improving and expanding the detection and removal of CSAM from online platforms, and ensuring that they are accurate, efficient, and scalable.
Increasing and diversifying the research and innovation on generative ML models and CSAM, and ensuring that they are interdisciplinary, collaborative, and transparent. Raising and spreading the awareness and education on generative ML models and CSAM, and ensuring that they are accessible, inclusive, and empowering.
The report concludes by emphasizing the urgency and importance of addressing the threat of generative ML models for CSAM, and the need for more collective and coordinated action and cooperation from all stakeholders, such as the tech industry, the government, the civil society, and the research community. The report also acknowledges the limitations and challenges of the current state of knowledge and practice on generative ML models and CSAM, and the opportunities and directions for future research and action on this topic.
A new report by researchers from the Stanford Internet Observatory and Thorn reveals how generative machine learning models, which can create realistic images, videos, and texts, are exacerbating the problem of online sexual exploitation of children. The report examines the current state and future trends of generative ML models, and their potential impacts and risks for CSAM production and consumption. The report also provides recommendations and best practices for mitigating the harm of generative ML models for CSAM, and calls for more collaboration and action from various stakeholders, such as the tech industry, the government, the civil society, and the research community.