Equalized Odds

Equalized odds is a fairness criterion in machine learning aiming to ensure that a model's predictions are equally accurate for different demographic groups, regardless of the true outcome. Current research focuses on developing algorithms and post-processing techniques, such as threshold optimization and score calibration, to achieve equalized odds while maintaining predictive accuracy, often using methods like adversarial learning or optimal transport. This work is crucial for mitigating bias in high-stakes applications like loan applications, hiring, and healthcare, where unfair algorithms can perpetuate societal inequalities. The ongoing challenge lies in balancing the often-conflicting goals of fairness, accuracy, and the practical limitations of real-world data.