Minority Inclusion

Minority inclusion in machine learning focuses on addressing the performance disparities of AI systems when dealing with underrepresented groups, aiming to improve fairness and accuracy across all populations. Current research explores techniques like data augmentation (e.g., diffusion-based methods), novel loss functions (e.g., majority-minority learning), and semi-supervised learning approaches, often employing generative adversarial networks or contrastive pre-training to synthesize or leverage existing data effectively. These advancements are crucial for mitigating bias in various applications, from medical diagnosis and natural language processing to recommendation systems and robotics, ultimately leading to more equitable and reliable AI systems.