Large language models in an app: Conducting a qualitative synthetic data analysis of how Snapchat's 'My AI' responds to questions about sexual consent, sexual refusals, sexual assault, and sexting
Fifteen researchers submitted a standardized set of questions about sexual consent, sexual refusals, sexual assault, and sexting to Snapchat's 'My AI' chatbot, then conducted a qualitative content analysis of the responses, cross-checking a subset against Llama and Gemini outputs. The study assesses whether a widely-used consumer chatbot's answers to sexual-health and disclosure-adjacent questions align with sexual health education literature.
Publisher
Journal of Sex Research (Taylor & Francis)
Published
10 Sept 2024
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today
Key Findings
- My AI's responses to sexual consent, refusal, and assault questions were generally consistent with sexual health education literature and pointed users toward trusted adults or resources
- Responses were often succinct and somewhat generalized, with variability in reading level, tone, and depth across similar question types
- The authors argue chatbot responses to sexual-assault-adjacent disclosure did not consistently reflect trauma-informed communication practices
Methodology Notes
Qualitative synthetic data analysis: 15 researchers independently submitted an identical set of standardized questions to Snapchat's My AI; outputs were coded via qualitative content analysis. A subset of questions was also run against Meta's Llama and Google's Gemini for comparison. Self-report/synthetic-query design; not a study of real user disclosures.
Sources
PMC Open Access Full Text (primary)
Archived snapshot (Wayback Machine) — preserved against link rot
Authors
Tiffany L. Marcantonio, Gracie Avery, Anna Thrash, Ruschelle M. Leone
Tags
Cite This
APA
Tiffany L. Marcantonio et al. (2024). Large language models in an app: Conducting a qualitative synthetic data analysis of how Snapchat's 'My AI' responds to questions about sexual consent, sexual refusals, sexual assault, and sexting. Journal of Sex Research (Taylor & Francis). https://pmc.ncbi.nlm.nih.gov/articles/PMC11891083/