Fairness in Ranking: Robustness through Randomization without the Protected Attribute

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.ai cs.cy cs.lg fairness machine machine learning online advertising randomization ranking recommender systems robustness systems through type work