Sensitive Attribute

Sensitive attributes, such as race, gender, or age, pose significant challenges in machine learning due to their potential to introduce bias and privacy violations. Current research focuses on mitigating these issues through various techniques, including data pre-processing methods (e.g., synthetic data generation, attribute disentanglement), model training modifications (e.g., adversarial training, fairness-aware regularization), and post-processing algorithms (e.g., demographic parity constraints). These efforts aim to create fairer and more privacy-preserving AI systems, impacting fields like healthcare, finance, and criminal justice by reducing discriminatory outcomes and protecting sensitive user information.