  Generative AI systems are increasingly capable of expressing emotions via
text and imagery. Effective emotional expression will likely play a major role
in the efficacy of AI systems -- particularly those designed to support human
mental health and wellbeing. This motivates our present research to better
understand the alignment of AI expressed emotions with the human perception of
emotions. When AI tries to express a particular emotion, how might we assess
whether they are successful? To answer this question, we designed a survey to
measure the alignment between emotions expressed by generative AI and human
perceptions. Three generative image models (DALL-E 2, DALL-E 3 and Stable
Diffusion v1) were used to generate 240 examples of images, each of which was
based on a prompt designed to express five positive and five negative emotions
across both humans and robots. 24 participants recruited from the Prolific
website rated the alignment of AI-generated emotional expressions with a text
prompt used to generate the emotion (i.e., "A robot expressing the emotion
amusement"). The results of our evaluation suggest that generative AI models
are indeed capable of producing emotional expressions that are well-aligned
with a range of human emotions; however, we show that the alignment
significantly depends upon the AI model used and the emotion itself. We analyze
variations in the performance of these systems to identify gaps for future
improvement. We conclude with a discussion of the implications for future AI
systems designed to support mental health and wellbeing.
