Gender Fairness

Gender fairness in machine learning (ML) models, particularly those used in healthcare and other high-stakes applications, is a critical area of research focusing on mitigating biases that lead to unequal outcomes for different genders. Current investigations utilize various ML architectures, including large language models (LLMs) and reinforcement learning (RL), to analyze both quantitative and qualitative aspects of fairness, often examining performance disparities across different datasets and cultural contexts. These efforts aim to improve the reliability and ethical implications of ML systems by identifying and addressing biases that may perpetuate or exacerbate existing societal inequalities, ultimately leading to more equitable and effective applications.