Songqi Zhou

FairNet: Dynamic Fairness Correction without Performance Loss via Contrastive Conditional LoRA

Ensuring fairness in machine learning models is a critical challenge. Existing debiasing methods often compromise performance, rely on static correction strategies, and struggle with data sparsity, particularly within minority groups. Furthermore, their utilization of sensitive attributes is often s…