Before revision:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, the fairness in retrieved contents significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.
After revision:  Our results on the MS MARCO benchmark show that, while the fairness of all ranking models is lower than the ones of ranker-agnostic baselines, and (2) the fairness of Bert rankers significantly improves when applying the proposed adversarial training . Lastly, we investigate the trade-off between fairness and utility, showing that through applying a combinatorial model selection method, we can maintain the significant improvements in fairness without any significant loss in utility.