A Human-Centered Approach to Improving Adolescent Real-Time Online Risk Detection Algorithms

Abstract

Computational approaches to detect the online risks that the youth encounter have presented promising potentials to protect them online. However, a major identified trend among these approaches is the lack of human-centered machine learning (HCML) aspect. It is necessary to move beyond the computational lens of the detection task to address the societal needs of such a vulnerable population. Therefore, I direct my attention in this dissertation to better understand youths' risk experiences prior to enhancing the development of risk detection algorithms by 1) Examining youths' (ages 13-17) public disclosures about sexual experiences and contextualizing these experiences based on the levels of consent (i.e., consensual, non-consensual, sexual abuse) and relationship types (i.e., stranger, dating/friend, family), 2) Moving beyond the sexual experiences to examine a broader array of risks within the private conversations of youth (N = 173) between 13 and 21 and contextualizing the dynamics of youth online and offline risks and the self-reports of risk experiences to the digital trace data, and 3) Building real-time machine learning models for risk detection by creating a contextualized framework. This dissertation provides a human-centered approach for improving automated real-time risk predictions that are derived from a contextualized understanding of the nuances relative to youths' risk experiences.