Pairwise Fairness for Ranking and Regression

By Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, Serena Wang. 2019

130 citations recorded by the source corpus.

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classification. Experiments illustrate the broad applicability and trade-offs of these methods.

Subfields: Safety Research

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Citations in the corpus

Beyond Individual and Group Fairness (2020, background): “There has also been work, however, studying fairness criteria and algorithms for ranking [Celis et al., 2018, Beutel et al., 2019, Narasimhan et al., 2019] and clustering problems [Chierichetti et al., 2017, Schmidt et al., 2018, Backurs et al., 2019] with requirement of a proportional…”
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals (2018, background): “Rate Constraints For Ranking or Regression Models Recent work has shown that the presented rate constraint methodology can be intuitively extended to regression and ranking models by defining rate constraints on pairs of examples, forming, for example, analogous pairwise fairness definitions (Narasi”
A Review on Fairness in Machine Learning (2022, background): “…a ranking function is considered to obey pairwise fairness if the likelihood of a clicked item being ranked above another relevant unclicked item is the same across groups (other works that follow the notion of pairwise fairness are, e.g., those of Kuhlman et al. [126] and Narasimhan et al. [156]).”
A General Approach to Fairness with Optimal Transport (2020, background): “…al. 2012; Feldman et al. 2015; Goh et al. 2016; Chouldechova 2017; Corbett-Davies et al. 2017; Gajane and Pechenizkiy 2017; Kusner et al. 2017; Cotter et al. 2018; Mitchell, Potash, and Barocas 2018; Verma and Rubin 2018; Zhang and Bareinboim 2018; Chi-appa and Isaac 2019; Narasimhan et al. 2020).”
Robust Optimization for Fairness with Noisy Protected Groups (2020, background): “Recent work has extended these group-based metrics to more general ranking and regression settings (Narasimhan et al., 2020).”
User-oriented Fairness in Recommendation (2021, background): “As concluded in [32], the metrics include the supervised criteria which control the average exposure of groups to be proportional to the average relevance of the results of groups to a query [8, 39], and the unsupervised criteria which require that the average exposure at the top of the ranking list”
A Survey on the Fairness of Recommender Systems (2022, methodology): “Grouped by Concept Consistent Fairness CO similar individuals / different groups should receive similar outcomes [30, 36, 39, 48, 53, 54, 91, 93, 95, 97] [6, 28, 45, 68, 73, 86, 90, 107, 112, 116] [12, 22, 25, 43, 44, 52, 64, 85, 104, 114] [8, 20, 58, 60, 61, 75, 76, 78, 83, 94, 102] [82, 99, 106]”