Emotional Images: Assessing Emotions in Images and Potential Biases in Generative Models

Abstract: This paper examines potential biases and inconsistencies in emotional evocation of images produced by generative AI models and their potential bias toward negative emotions. In particular, 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. As a first step, the study evaluates three approaches for identifying emotions in images -- traditional supervised learning, zero-shot learning with vision-LLMs, and cross-modal auto-captioning -- using EmoSet, a large dataset of image-emotion annotations that categorizes images across eight emotional types. Results show fine-tuned models, particularly Google's Vision Transformer (ViT), significantly outperform zero-shot and caption-based methods in recognizing emotions in images. For a cross-modality comparison, we then analyze the differences between emotions in text prompts -- via existing text-based emotion-recognition models -- and the emotions evoked in the resulting images. Findings indicate that AI-generated images frequently lean toward negative emotional content, regardless of the original prompt. This emotional skew in generative models could amplify negative affective content in digital spaces, perpetuating its prevalence and impact. The study advocates for a multidisciplinary approach to better align AI emotion recognition with psychological insights and address potential biases in generative AI outputs across digital media.