Year: 2025
Type: article
Abstract: Generative artificial intelligence (AI) systems can produce text, images, videos, and audio in response to prompts. They are increasingly applied across various domains, including intimacy and sexuality—ranging from AI-generated pornography to sexual counseling via AI chatbots. While AI-generated content holds significant potential, it is also met with skepticism. Anti-AI bias is defined as a systematic tendency to evaluate AI-produced outputs more negatively than equivalent human-created content, regardless of actual quality. Following the experimental labeling paradigm, this study examined whether identical couple images (H 1a ) and couple counseling excerpts (H 2a ) were evaluated less favorably when labeled as AI-generated rather than human-created, and whether AI attitudes and AI literacy moderated these effects for images (H 1b ) and counseling dialogues (H 2b ). Two consecutive online experiments were conducted in 2024 with a national sample of adults in Germany ( N = 2,658). In Experiment 1, identical romantic couple images received less positive evaluations when labeled as AI-generated images versus as human-generated photographs ( d = .21; H 1a ). In Experiment 2, identical sexuality-related couple counseling excerpts labeled as involving an AI counselor were rated less favorably than those labeled as involving a human counselor ( d = .23; H 2a ). AI attitudes and AI literacy combined did neither moderate the labeling effect for images (η 2 = .005; H 1b ) nor for counseling dialogues (η 2 = .003; H 2b ). These findings extend the literature on anti-AI bias into intimate contexts. Nevertheless, user dispositions toward AI remain an important consideration when designing and implementing generative AI systems in intimacy- and sexuality-related domains.
Source: Archives of Sexual Behavior
Authors: Nicola Döring, M. Rohangis Mohseni
Institution: Technische Universität Ilmenau
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
Funder: Technische Universität Ilmenau
OpenAlex ID: W4414959757