Ranking Fairness

Fair ranking aims to create ranking systems (like recommender systems or search engines) that are unbiased across different subgroups, ensuring equitable representation and avoiding discriminatory outcomes. Current research focuses on developing novel metrics to measure fairness in ranked lists, exploring model-agnostic post-processing techniques and in-processing regularization methods to improve fairness while maintaining accuracy, and employing algorithms like the Sinkhorn algorithm for efficient computation. This field is crucial for mitigating bias in widely used systems, impacting areas like online marketplaces, information retrieval, and healthcare, where fairness is paramount for ethical and equitable outcomes.