The development of generative artificial intelligence (GAI) has democratized access to high-quality image generation, creating both opportunities and risks. Research shows that generative adversarial networks (GANs) can generate faces that are nearly indistinguishable from and more trustworthy than real ones. However, the literature currently contains no direct comparison of the realism and trustworthiness of GAN-synthesized faces and faces produced by more recent models. Here we test whether faces generated by diffusion models (DMs), a new and more powerful GAI architecture, elicit trust and have similarly passed through the uncanny valley. In Experiment 1, participants (N = 169) saw 96 facial images and for each face indicated whether it was real or AI generated. In Experiment 2, participants (N = 87) again saw 96 facial images and this time were asked to rate how trustworthy each face appeared. We find that DMs produce less photo-realistic faces than GANs, but those faces are rated as more trustworthy than GANs and real faces. As the realism and availability of GAI continue to increase, it is now-more than ever-critical to understand this threat and develop strategies to mitigate potential harms to individuals, organizations, and democracies.
McGuire et al. (Tue,) studied this question.