AI-generated images can perpetuate biases and stereotypes, impacting brand trust and customer engagement. Entire groups can be erased, like when AI generates images of "a successful person" and overwhelmingly produces young white men in suits. AI can also exaggerate biases, producing hypersexualised images of Asian women while generating professional-looking avatars for their male colleagues. These biases aren't just technical issues – they affect how people see themselves and others, influencing hiring decisions, media narratives, and self-perception.
The biases in AI-generated images can have far-reaching consequences. Tools like Stable Diffusion and DALL-E skew results, showing almost entirely male and light-skinned software developers. This underrepresentation and stereotyping can erode trust, hinder customer connection, and pose regulatory risks. For marketers, it's crucial to understand the potential impact of biased AI-generated images on their brand and audience.
To address AI visual bias, it's essential to audit your AI pipeline and check outputs for skewed patterns. Knowing where generative tools are used is vital, and human review is necessary for high-visibility content. Editing tools can be used wisely to re-balance representation without distorting reality. Adjusting cropping, masking, or backgrounds can make a significant difference. By taking these steps, companies can develop fairer AI systems and create more inclusive, accurate, and powerful visuals.
By staying informed about the fast-moving conversation around AI ethics, companies can pivot when issues arise and create more inclusive AI-generated images. This can help them stand out in a crowded market and build trust with their audience. Ultimately, addressing biases in AI-generated images is crucial for creating fairer AI systems and promoting inclusivity and accuracy in visual representation.
