Ashwaq Alsoubai
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.
Footnotes
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- A Human-Centered Approach to Improving Adolescent Real-Time Online Risk Detection Algorithms
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