New approach generalizes fair top-k selection with multiple groups

Researchers have developed a new approach to fair top-k selection, which aims to ensure proportional representation for minority groups among selected candidates. This generalized method considers multiple protected groups and seeks to minimize disparity from a reference scoring function. While the problem can become computationally intractable with multiple groups, the researchers identified a gap in the hardness barrier that allows for efficient solutions when the number of groups is small and k is also small. The study also introduces a new disparity measure called utility loss, which may lead to more stable scoring functions, and demonstrates strong empirical performance on real-world datasets. AI

排序理由 The cluster contains an academic paper detailing a new algorithmic approach to a specific problem in computer science. [lever_c_demoted from research: ic=1 ai=0.4]

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• Guangya Cai

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