  In the domain of text-to-image generative models, the inadvertent propagation
of biases inherent in training datasets poses significant ethical challenges,
particularly in the generation of socially sensitive content. This paper
introduces EquiPrompt, a novel method employing Chain of Thought (CoT)
reasoning to reduce biases in text-to-image generative models. EquiPrompt uses
iterative bootstrapping and bias-aware exemplar selection to balance creativity
and ethical responsibility. It integrates iterative reasoning refinement with
controlled evaluation techniques, addressing zero-shot CoT issues in sensitive
contexts. Experiments on several generation tasks show EquiPrompt effectively
lowers bias while maintaining generative quality, advancing ethical AI and
socially responsible creative processes.Code will be publically available.
