Optimal Fair

Optimal fair classification aims to develop machine learning models that achieve high accuracy while mitigating unfair bias against protected groups, often measured by metrics like demographic parity, equal opportunity, or predictive equality. Current research focuses on developing algorithms, including post-processing methods and optimization techniques, that efficiently find classifiers minimizing error subject to fairness constraints, often employing linear programming or Wasserstein barycenter approaches. This work is crucial for ensuring fairness in high-stakes applications like loan applications or criminal justice, and its findings are informing the development of more equitable and reliable AI systems.