Supplementary Material Fairness in Ranking under Uncertainty A Related Work

The group fairness perspective imposes constraints like demographic parity (Calders et al., 2009; Zliobaite, 2015) and equalized odds (Hardt et al., 2016). Although similar in spirit, our work sidesteps this need to define a similarity metric between agents in the feature space. Rather, we view an agent's Ranking has been widely studied in the field of Information Retrieval (IR), mostly in the context of optimizing user utility. The Probability Ranking Principle (PRP) (Robertson, 1977), a guiding principle for ranking in IR, states that user utility is optimal when documents (i.e., the agents) are Besides ranking diversity, IR methods have dealt with uncertainty in relevance that comes via users' implicit or explicit feedback (Penha and Hauff, 2021; Soufiani et al., 2012), as well as stochasticity arising Kearns et al. (2017) present a way to fairly select Hence, they propose using the true CDF rank as a derived merit criterion that can be compared. Thus, a fair principal stands to gain more by obtaining perfect information.