Stability and Multigroup Fairness in Ranking with Uncertain Predictions

arXiv:2402.09326v1 Announce Type: new
Abstract: Rankings are ubiquitous across many applications, from search engines to hiring committees. In practice, many rankings are derived from the output of predictors. However, when predictors trained for classification tasks have intrinsic uncertainty, it is not obvious how this uncertainty should be represented in the derived rankings. Our work considers ranking functions: maps from individual predictions for a classification task to distributions over rankings. We focus on two aspects of ranking functions: stability to perturbations …

abstract applications arxiv classification cs.lg fairness hiring intrinsic practice predictions ranking rankings search stability tasks type uncertain uncertainty