  This study addresses gender bias in image generation models using
Reinforcement Learning from Artificial Intelligence Feedback (RLAIF) with a
novel Denoising Diffusion Policy Optimization (DDPO) pipeline. By employing a
pretrained stable diffusion model and a highly accurate gender classification
Transformer, the research introduces two reward functions: Rshift for shifting
gender imbalances, and Rbalance for achieving and maintaining gender balance.
Experiments demonstrate the effectiveness of this approach in mitigating bias
without compromising image quality or requiring additional data or prompt
modifications. While focusing on gender bias, this work establishes a
foundation for addressing various forms of bias in AI systems, emphasizing the
need for responsible AI development. Future research directions include
extending the methodology to other bias types, enhancing the RLAIF pipeline's
robustness, and exploring multi-prompt fine-tuning to further advance fairness
and inclusivity in AI.
