Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback

Extended Abstract In this research project, the focus is on addressing the critical issue of stereotype bias in image generation models, particularly gender bias, which poses significant ethical implications. Leveraging the potential of Reinforcement Learning from Artificial Intelligence Feedback (RLAIF), a novel pipeline using Denoising Diffusion Policy Optimization (DDPO) is proposed to fine-tune image generation models and mitigate gender bias. The study utilizes a pretrained stable diffusion model and a gender classification Transformer model to evaluate bias in generated images. The gender classification model achieved high accuracy, reaching 100% in specific tests. Experiments showcase the pipeline's ability to reach stable gender balance, indicating the potential of RLAIF for bias reduction in image generation models.