Deploying human-centered machine learning to improve adolescent online sexual risk detection algorithms

Abstract

As adolescents' engagement increases online, it becomes more essential to provide a safe environment for them. Although some apps and systems are available for keeping teens safer online, these approaches and apps do not consider the needs of parents and teens. We would like to improve adolescent online sexual risk detection algorithms. In order to do so, I'll conduct three research studies for my dissertation: 1) Qualitative analysis on teens posts on an online peer support platform about online sexual risks in order to gain deep understanding of online sexual risks 2) Train a machine learning approach to detect sexual risks based on teens conversations with sex offenders 3) develop a machine learning algorithm for detecting online sexual risks specialized for adolescents.