Engaging with ML/AI: Combating Child Sexual Abuse | Rebecca Portnoff

Head of Data Science, Thorn

Talk Title: Engaging with ML/AI: Combating Child Sexual Abuse

Abstract: Offline and online sexual harms against children have been accelerated by the internet. The child safety ecosystem is overtaxed; in 2022 reports to the National Center for Missing and Exploited Children (NCMEC) contained over 88 million files of child sexual abuse material (CSAM) and other files related to child sexual exploitation.

Thorn is a non-profit that is dedicated to building technology to combat child sexual abuse. Our work at Thorn is focused on: accelerating victim identification, stopping re-victimization (the viral spread of CSAM) and preventing abuse from happening in the first place. Machine Learning/AI plays an important role in this work. In this talk, we will give an overview of some of this technology we’ve built at Thorn, how we prioritize wellness in building this technology, and how we collaborate cross-functionally to deploy this technology internally and externally to have impact.

Bio: Dr. Rebecca Portnoff has dedicated her career to building tools and driving initiatives to defend children from sexual abuse. She is currently Head of Data Science at Thorn, owning strategy and vision for Data Science across the organization and leading Thorn’s engagement with machine learning/artificial intelligence (ML/AI) as a field. She works cross functionally with business and technical functions to develop, deploy and maintain ML/AI to: accelerate victim identification, stop re-victimization, and prevent abuse from occurring.

Rebecca brings with her over a decade of experience in ML/AI, child safety, and trauma-informed leadership. She is also an avid musician, singing jazz and playing the violin in her free time. She holds a B.S.E. in Computer Science from Princeton University, where she also minored in vocal jazz, and a Ph.D. in Computer Science from UC Berkeley.