Measuring Technology-Facilitated Sexual Violence: The Impacts of Artificial Intelligence

Technology-Facilitated Sexual Violence (TFSV) is a pervasive public health issue, in which digital communication technologies, such as social media and messaging apps, are used to enact sexual violence and online harassment. Rapid technological advancement continually reshapes TFSV, demanding up-to-date research. In recent years, artificial intelligence (AI) has emerged as a new tool for perpetuating TFSV, through the creation of AI-generated nude images and deepfakes. However, existing TFSV measures do not currently assess AI-specific harms, hindering evidence-based prevention, intervention, or policy development. The purpose of this dissertation is to advance the measurement of sexual violence by developing an updated, valid and reliable measure of TFSV that incorporates contemporary understandings, including AI-enabled harms. Specifically, the aims are: 1) systematically review existing measures of sexual violence, 2) explore how individuals with TFSV experiences conceptualize its different dimensions, and 3) test the psychometric properties of the measure. Chapter 1 outlines key definitions, prevalence data, health impacts, and measurement challenges related to sexual violence and TFSV. Chapter 2 is a systematic review that examined the current state of measurement of sexual violence. A total of 38 unique measures across 44 studies were identified and analyzed using an adapted framework for intimate partner violence research. Findings highlight the need for consistent, inclusive, and contextually relevant measurement tools. Chapter 3 presents a qualitative study, involving 24 participants, whose interviews informed the development of new and revised items for the measure. Participants shared both personal and observed experiences of TFSV, including AI-enabled harms and new dimensions not captured in existing conceptual frameworks. Chapter 4 details the quantitative study, testing the measure with 386 participants. Exploratory factor analysis yielded four factors: 1) Unwanted sexual comments, 2) AI-related TFSV, 3) Online sexual coercion, and 4) Online stalking. The resulting Index of TFSV Behaviors (ITB) represents the first measure to intentionally include AI-related harms. Chapter 5 synthesizes findings and implications, emphasizing the importance of diverse samples and broad conceptual approaches. This dissertation demonstrates how updated, inclusive measures can capture emergent forms of TFSV, providing essential tools to guide prevention, intervention, and policy efforts.