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Combining the preferences of many rankers into one single consensus rankingis critical for consequential applications from hiring and admissions tolending. While group fairness has been extensively studied for classification,group fairness in rankings and in particular rank aggregation remains in itsinfancy. Recent work introduced the concept of fair rank aggregation forcombining rankings but restricted to the case when candidates have a singlebinary protected attribute, i.e., they fall into two groups only. Yet itremains an open problem how to create a consensus ranking that represents thepreferences of all rankers while ensuring fair treatment for candidates withmultiple protected attributes such as gender, race, and nationality. In thiswork, we are the first to define and solve this open Multi-attribute FairConsensus Ranking (MFCR) problem. As a foundation, we design novel groupfairness criteria for rankings, called MANI-RANK, ensuring fair treatment ofgroups defined by individual protected attributes and their intersection.Leveraging the MANI-RANK criteria, we develop a series of algorithms that forthe first time tackle the MFCR problem. Our experimental study with a richvariety of consensus scenarios demonstrates our MFCR methodology is the onlyapproach to achieve both intersectional and protected attribute fairness whilealso representing the preferences expressed through many base rankings. Ourreal-world case study on merit scholarships illustrates the effectiveness ofour MFCR methods to mitigate bias across multiple protected attributes andtheir intersections. This is an extended version of "MANI-Rank: MultipleAttribute and Intersectional Group Fairness for Consensus Ranking", to appearin ICDE 2022.

MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Ranking