International Journal of Human-Computer Studies · 2024 · 46 citations · 36 references
ComputingComputer ScienceArtificial IntelligenceNatural Language ProcessingVisual PerceptionAffective ComputingMental ImageryGenerative Image SynthesisImagery GenerationText-to-image GenerationText-to-image TranslationUser-generated Images
Generative Artificial Intelligence (Gen-AI) has rapidly advanced in recent years, potentially producing enormous impacts on industries, societies, and individuals in the near future. In particular, Gen-AI text-to-image models allow people to easily create high-quality images possibly revolutionizing human creative practices. Despite their increasing use, however, the broader population's perceptions and understandings of Gen-AI-generated images remain understudied in the Human-Computer Interaction (HCI) community. This study investigates how individuals, including those unfamiliar with Gen-AI, perceive Gen-AI text-to-image (Stable Diffusion) outputs. Study findings reveal that participants appraise Gen-AI images based on their technical quality and fidelity in representing a subject, often experiencing them as either prototypical or strange: these experiences may raise awareness of societal biases and evoke unsettling feelings that extend to the Gen-AI itself. The study also uncovers several “relational” strategies that participants employ to cope with concerns related to Gen-AI, contributing to the understanding of reactions to uncanny technology and the (de)humanization of intelligent agents. Moreover, the study offers design suggestions on how to use the anthropomorphizing of the text-to-image model as design material, and the Gen-AI images as support for critical design sessions.
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