The requirement that, for fair document retrieval, the documents should be ranked in the order to equally expose authors and organizations has been studied for some years. The fair exposure of a ranking, however, undermines the optimality of the Probability Ranking Principle and as a consequence retrieval effectiveness. It is shown how the variations of fairness and effectiveness can be related by a model. To this end, the paper introduces a fairness measure inspired in Gini’s index of mutability for non-ordinal variables and relates it to a general enough measure of effectiveness, thus modeling the connection between these two dimensions of Information Retrieval. The paper also introduces the measurement of the statistical significance of the fairness measure. An empirical study completes the paper.

为了公平的文档检索，应该按照平等地公开作者和组织的顺序对文档进行排序的要求已经研究了多年。然而，排名的公平公开会破坏概率排名原理的最优性，从而破坏检索的有效性。它展示了如何通过模型将公平性和有效性的变化联系起来。为此，本文引入了受非序数变量基尼系数可变性指数启发的公平性度量，并将其与足够普遍的有效性度量相关联，从而对信息检索的这两个维度之间的联系进行了建模。论文还介绍了公平性测度的统计显着性的测量。实证研究完成了本文。