Fair Ranking

Fair ranking aims to design ranking systems that prioritize both relevance and fairness, addressing biases that can disproportionately disadvantage certain groups or individuals. Current research focuses on developing algorithms and models, such as those based on ordered weighted averages and Plackett-Luce models, that incorporate fairness constraints while maintaining high ranking quality, often through techniques like knowledge distillation or constrained optimization. This field is crucial for mitigating bias in high-stakes applications like job searches, clinical trial site selection, and online platforms, impacting both the scientific understanding of fairness and the ethical design of real-world systems.