Fair Classification

Fair classification aims to develop machine learning models that make accurate predictions while avoiding discriminatory outcomes across different demographic groups. Current research focuses on integrating fairness constraints directly into the training process using techniques like reinforcement learning and adaptive boosting, as well as post-processing methods that adjust model outputs to satisfy various fairness criteria (e.g., demographic parity, equal opportunity). This field is crucial for mitigating bias in high-stakes applications like loan applications and criminal justice, ensuring equitable treatment and promoting fairness in automated decision-making systems.