March 29, 2024, 4:42 a.m. | Andrii Kliachkin, Eleni Psaroudaki, Jakub Marecek, Dimitris Fotakis

cs.LG updates on

arXiv:2403.19419v1 Announce Type: new
Abstract: There has been great interest in fairness in machine learning, especially in relation to classification problems. In ranking-related problems, such as in online advertising, recommender systems, and HR automation, much work on fairness remains to be done. Two complications arise: first, the protected attribute may not be available in many applications. Second, there are multiple measures of fairness of rankings, and optimization-based methods utilizing a single measure of fairness of rankings may produce rankings that …

abstract advertising arxiv automation classification cs.lg fairness machine machine learning online advertising randomization ranking recommender systems robustness systems through type work

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