Exposing the Hidden Biases of AI Image Generators
Manos Plitsis & Giorgos Bouritsas
If one asks an AI image generator like Stable Diffusion or DALL·E to picture "a doctor", they'll probably get a picture of a man. If they ask for "a nurse", they'll probably get a woman. AI models, among other technological artefacts, are known to inherit common stereotypes from their human creators (designers, developers, data collectors, annotators, etc.).
Researchers and investigative journalists have been documenting these stereotypes for years, putting pressure on the academic community and AI companies to "debias" their models, either via retraining or by internal adjustments, to give more balanced results. But here is the catch: these fixes mostly work on the typical prompts investigators test, e.g., "a photo of a doctor" or "an engineer at work", generic sentences that reflect societal biases we are mostly aware of.
What happens when real users type something more descriptive instead: "a scientist with intense focus" or "a doctor with compassionate eyes"?
