Generating Fearful Images: Investigating Potential Emotional Biases in Image-Generation Models

This paper examines potential biases and inconsistencies in the emotions evoked by images produced by generative artificial intelligence (AI) models and their potential bias toward negative emotions. We assess this bias by comparing the emotions evoked by an AI-produced image to the emotions evoked by prompts used to create those images. After developing and validating automated methods for emotion recognition across modalities, we examine correlations in the prevalence of emotions across text and images and measure the degree to which generative AI models tend to over-represent specific emotions in the resulting images. Findings indicate that AI-generated images from Stable Diffusion models are biased towards producing images that evoke fear, regardless of the original prompt, as metrics show a significant over-representation of that emotion compared to five other emotions. We extend this analysis to a more recent enterprise-level models, such as ChatGPT and Gemini, and find similar results, suggesting a systemic bias rather than one present only in a single model. While certain limitations in the alignment of emotions across modalities limit this work, the emotional skew we find in generative models is consistent with an over-representation of fearful content in training data, and this bias could amplify negative affective content in digital spaces further, perpetuating its prevalence and impact.