In this paper, we take an axiomatic approach to define random group-fair rankings that satisfy a natural set of consistency and fairness axioms. We show that this leads to a unique distribution $\mathcal{D}$ over rankings obtained by merging given ranked list of items from different sensitive demographic groups while satisfying given lower and upper bounds on the representation of each group in the top ranks. Randomized or stochastic rankings have been of interest in recent literature for offering better fairness and robustness than deterministic rankings. Our problem formulation works even when there is implicit bias, incomplete relevance information, or when only ordinal ranking is available instead of relevance scores or utility values. We propose three algorithms to sample a random group fair ranking from the distribution $\mathcal{D}$ mentioned above. Our first algorithm samples rankings from a distribution $\epsilon$-close to $\mathcal{D}$ in total variation distance, and has expected running time polynomial in all input parameters and $1/\epsilon$, when there is a sufficient gap between upper and lower bound representation constraints for all the groups. Our second algorithm samples rankings from $\mathcal{D}$ exactly, in time exponential in the number of groups. Our third algorithm samples random group fair rankings from $\mathcal{D}$ exactly and is faster than the first algorithm when the gap between upper and lower bounds on the representation for each group is small. We experimentally validate the above guarantees of our algorithms for group fairness in top ranks and representation in every rank on real-world data sets.
翻译:在本文中, 我们用一种不言而喻的方法来定义随机的组公平等级, 以达到自然的一致性和公平性。 我们显示, 这导致在合并不同敏感人口组的排名列表中, 符合每个组在最高级别中代表的下限和上限, 随机化或随机性排序对最近的文献感兴趣, 以提供比确定性排名更好的公平和稳健性。 我们的问题配置即使在存在隐含的偏差、 不完全的关联信息, 或只有正统性排名, 而不是相关分数或实用值的情况下, 也起作用。 我们建议使用三种算法, 抽取一个随机的组公平排名 $\ mathca{ D}, 同时满足对每个组中每个组的分布 $- 接近到 $ mathcal{ D} 的上限值。 在所有组中, 当每个组的上限值和下定级中, 我们的组的上限数和下级数中, 我们的组的上级数和下级数组的上级数 。