Underrepresented Group

Underrepresentation in data, across various domains from medical imaging to natural language processing, poses a significant challenge to the fairness and generalizability of machine learning models. Current research focuses on mitigating this bias through techniques like data augmentation (using generative models such as GANs and diffusion models), re-weighting algorithms, and the development of fairness-aware model architectures. Addressing this issue is crucial for ensuring equitable access to technological advancements and reliable model performance across diverse populations, impacting fields ranging from healthcare diagnostics to social justice applications.