AI disclosure labels change how people look at faces
EEG and Eye-Tracking Evidence That AI Disclosure Shapes Face Evaluation
Human-Computer InteractionArtificial Intelligence
Summary
It can be hard to tell AI-made faces from real ones just by looking. The authors studied how people react to faces when they are told if the face is made by AI or a human. They found that people’s brains pay more attention to the label than the face itself and that if the label doesn’t match the face’s source, viewers rethink what they see. Labels influence how people visually explore faces even if their feelings about the face do not change.
What this means in practice
- •For user interface designers: Design disclosure labels that effectively guide user attention and influence how AI-generated content is processed visually.
- •For marketing teams: Craft AI product visuals with labels that shape viewer perceptions and reconsiderations of AI-generated images.
Authors
Teodora Mitrevska, Luise Donat, Andreas Butz, Thomas Kosch, Abdallah El Ali, Francesco Chiossi
Abstract
AI-generated faces can be difficult to distinguish from real ones, leaving viewers to rely on source labels when judging an image. Yet prior work has made it difficult to separate the effects of what an image actually is from what viewers are told it is. We validated faces as AI-generated or human in an online study (N=169), then crossed actual source (AI, human) with label (none, Made with AI, Made by a human) in a lab study $N=30), recording event-related potentials (ERPs) and gaze. ERP responses were equivalent for AI-generated and real faces, but varied with the label: labels drew early attention (N2), while labels that conflicted with the face's actual source prompted re-evaluation of the face (P3). Affective processing and initial gaze orienting were unchanged, but labels altered visual exploration. We provide a validated stimulus set and evidence that attributed origin shapes face processing, with implications for disclosure design.