Mitigating stereotypical biases in text to image generative systems
Piero Esposito Parmida Atighehchian Anastasis Germanidis Deepti Ghadiyaram
State-of-the-art generative text-to-image models are known to exhibit social
biases and over-represent certain groups like people of perceived lighter skin
tones and men in their outcomes. In this work, we propose a method to mitigate
such biases and ensure that the outcomes are fair across different groups of
people. We do this by finetuning text-to-image models on synthetic data that
varies in perceived skin tones and genders constructed from diverse text
prompts. These text prompts are constructed from multiplicative combinations of
ethnicities, genders, professions, age groups, and so on, resulting in diverse
synthetic data. Our diversity finetuned (DFT) model improves the group fairness
metric by 150% for perceived skin tone and 97.7% for perceived gender. Compared
to baselines, DFT models generate more people with perceived darker skin tone
and more women. To foster open research, we will release all text prompts and
code to generate training images.