AI-Generated Faces are Becoming More Trustworthy
Abstract
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. Here we test whether faces generated by Diffusion Models (DMs), a new and more powerful GAI architecture, have similarly passed through the uncanny valley. We find that DMs produce less photo-realistic faces than GANs, but those faces are rated as more trustworthy than GAN 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.
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