AI has a stereotyping problem

"(Image credit: Getty Images) Type 'board meeting' into even the best AI image generators and chances are high it will populate with rows of white men in suits. While headlines about AI often centre on hype or alarm, bias is already a very real and very present problem in visual media. And for brands, this isn't just about fairness. It's about trust, conversions, and whether your audience sees themselves in your story."

"There's no denying the upside: AI promises faster content creation, scalable campaigns, and limitless creative experimentation. But that promise comes with a catch: these systems learn from past imagery, which means they don't just reflect history, but they replicate its stereotypes, sometimes on repeat. To make it worse, images are more memorable than text. People might scroll past a biased sentence, but a biased image they remember. That makes misrepresentation in visuals even more powerful and more dangerous."

"Representation in AI images isn't just about aesthetics - it's about who gets included in the story. AI systems, like humans, can internalise implicit biases from their training data. If a model learns from biased language or imagery, it may unknowingly generate prejudiced or stereotypical outputs. Some of these biases include the following: Underrepresentation: Entire groups can be erased. When researchers asked an AI to generate images of "a successful person," it overwhelmingly produced young white men in suits"

AI image generation offers speed, scale, and creative possibilities but inherits biases from historical training imagery and language. Biased models underrepresent entire groups and amplify harmful stereotypes, producing memorable visuals that can mislead audiences and damage brand trust and conversions. Visual misrepresentation is more potent than biased text because images stick in viewers' minds. Mitigation requires deliberate dataset diversity, careful prompt design, human review, inclusive representation choices, testing across audience segments, transparency about model limitations, and ongoing auditing to prevent erasure and stereotyping in generated visuals.

Read at Creative Bloq