Firth Bias Reduction

Firth bias reduction aims to mitigate systematic errors in machine learning models, particularly those arising from skewed or limited training data, impacting model fairness and generalization. Current research focuses on applying this technique to various model architectures, including generative adversarial networks (GANs) and large language models (LLMs), often in conjunction with adaptive regularization strategies to optimize its effectiveness. Addressing these biases is crucial for improving the reliability and fairness of AI systems across diverse applications, ranging from natural language processing to medical diagnosis and beyond.