Comparing Apples and Oranges: Fairness and Diversity in Ranking (Invited Talk)

Author Julia Stoyanovich

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Julia Stoyanovich
  • New York University, NY, USA

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Julia Stoyanovich. Comparing Apples and Oranges: Fairness and Diversity in Ranking (Invited Talk). In 24th International Conference on Database Theory (ICDT 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 186, p. 2:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Algorithmic rankers take a collection of candidates as input and produce a ranking (permutation) of the candidates as output. The simplest kind of ranker is score-based; it computes a score of each candidate independently and returns the candidates in score order. Another common kind of ranker is learning-to-rank, where supervised learning is used to predict the ranking of unseen candidates. For both kinds of rankers, we may output the entire permutation or only the highest scoring k candidates, the top-k. Set selection is a special case of ranking that ignores the relative order among the top-k. In the past few years, there has been much work on incorporating fairness and diversity requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In my talk I will offer a broad perspective that connects formalizations and algorithmic approaches across subfields, grounding them in a common narrative around the value frameworks that motivate specific fairness- and diversity-enhancing interventions. I will discuss some recent and ongoing work, and will outline future research directions where the data management community is well-positioned to make lasting impact, especially if we attack these problems with our rich theory-meets-systems toolkit.

Subject Classification

ACM Subject Classification
  • Information systems → Data management systems
  • Theory of computation → Theory and algorithms for application domains
  • Social and professional topics → Computing / technology policy
  • fairness
  • diversity
  • ranking
  • set selection
  • responsible data management


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