<p>Generative AI for image creation emerges as a staple in the toolkit of digital artists, visual designers, and the general public who want to augment their representation toolkit. Social media users have many tools to shape their visual representation: image editing tools, filters, face masks, face swaps, avatars, and AI-generated images. The importance of the right image can not be understated: It is crucial for creating the right first impression, sustaining trust, and enabling communication. Conventionally accepted representations of individuals, groups, and collectives may help foster inclusivity, understanding, and respect in society, ensuring that diverse perspectives are acknowledged and valued. While previous research revealed the biases in large image datasets such as ImageNet and inherited biases in the AI systems trained on it, within this work, we look at the prejudices and stereotypes as they emerge from textual prompts used for generating images on Discord using the StableDiffusion model. We analyze over 1.8 million prompts depicting men and women and use statistical methods to uncover how prompts describing men and women are constructed and what words constitute the portrayals of respective genders. We show that the median description length of men in words is systematically shorter than that of women, while our findings also suggest that this holds true for any number of unique prompt writers. Regarding word and topic analysis, our findings suggest the existence of classic stereotypes in which men are described using dominant qualities such as “strong” and “rugged”. In contrast, women are represented with concepts related to body and submission: “beautiful”, “pretty”, etc. These results highlight the importance of considering the original intent of the prompting and suggest that cultural practices on platforms such as Discord should be considered when designing interfaces that promote exploration and fair representation.</p>