Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, people. Credit ratings, standardized tests, risk assessments output only a score, but are also used implicitly for ranking. Bias in such ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. In this paper, we formulate the problem of underranking in group-fair rankings, which was not addressed in previous work. Most group-fair ranking algorithms post-process a given ranking and output a group-fair ranking. We define underranking based on how close the group-fair rank of each item is to its original rank, and prove a lower bound on the trade-off achievable for simultaneous underranking and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove. Our algorithm works with group fairness constraints for any number of groups. Our experimental results confirm the theoretical trade-off between underranking and group fairness, and also show that our algorithm achieves the best of both when compared to the state-of-the-art baselines.
翻译:搜索和推荐系统,如搜索引擎、招聘工具、在线市场、新闻和社交媒体等搜索和建议系统、内容、产品和有时是人排名清单等产出。 信用评级、标准化测试、风险评估只产生一个分数,但也暗含地用于排名。 在这类排名制度中,特别是在高层中,偏见可能加剧社会和经济不平等,意见的两极分化,强化陈规定型观念。另一方面,对少数群体的偏见纠正,如果被认为有利于群体公平结果而不是精英管理,则可能造成更大的伤害。在本文中,我们提出了群体公平排名中排名过低的问题,而此前的工作没有解决这个问题。大多数群体公平排序算法处理后一个给定的等级,并输出出一个集团公平等级。我们根据集团公平级别如何接近其原有等级,界定排名过低的等级,并证明对可同时低级别和群体公平性进行更公平的交易。 我们的算法工作以集团公平性标准为基础,同时在集团内部实现最佳等级和集团之间的最高等级。