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We present a simple and versatile framework for evaluating ranked lists interms of group fairness and relevance, where the groups (i.e., possibleattribute values) can be either nominal or ordinal in nature. First, wedemonstrate that, if the attribute set is binary, our framework can easilyquantify the overall polarity of each ranked list. Second, by utilising anexisting diversified search test collection and treating each intent as anattribute value, we demonstrate that our framework can handle soft groupmembership, and that our group fairness measures are highly correlated withboth adhoc IR and diversified IR measures under this setting. Third, wedemonstrate how our framework can quantify intersectional group fairness basedon multiple attribute sets. We also show that the similarity function forcomparing the achieved and target distributions over the attribute valuesshould be chosen carefully.

A Versatile Framework for Evaluating Ranked Lists in terms of Group Fairness and Relevance