This research project focuses on improving the fairness, diversity, and quality of AI-generated images by training models to produce unbiased representations of individuals across various professions. The study explores challenges in generating fair and balanced depictions of workers, particularly in roles such as construction, fast food, and social work, where biases in gender representation and artistic styles were prevalent.
Key objectives include refining captioning strategies to reduce bias, enhancing the diversity of training data, and addressing technical limitations that influence image generation quality. The research employs tools like IP Adapter to mask or modify biased features in generated images, while also leveraging negative prompting to guide the model toward more cohesive and unbiased outputs.
The findings highlight significant progress in mitigating biases but also reveal ongoing challenges, such as the subjective nature of defining "unbiased" outcomes and the persistent influence of artistic styles and language prompts on image generation. This research underscores the importance of iterative testing, diverse datasets, and advanced tools to develop AI models capable of producing fair and inclusive representations across professions.
This project serves as a foundation for future work aimed at improving AI's ability to generate equitable and realistic depictions of individuals in various roles, ultimately contributing to more inclusive and unbiased applications of AI technology.