Gender Classification

Gender classification, the task of automatically determining an individual's gender from various data sources like images, audio, or text, aims to improve accuracy and fairness in applications ranging from forensic science to personalized services. Current research focuses on mitigating biases in existing models, particularly those stemming from skewed datasets and societal stereotypes, often employing deep learning architectures like convolutional neural networks (CNNs) and transformers, along with explainable AI (XAI) techniques to understand model decision-making. The field's significance lies in its potential to improve the accuracy and fairness of AI systems, while also raising crucial ethical considerations regarding bias and discrimination in algorithmic decision-making.