mlsanityData to 14 Oct 2026

Pairwise Fairness for Ordinal Regression

arXiv 2105.03153 ↗First posted 7 May 2021stat.ML cs.LGcited by 6

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Abstract (arXiv)

We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predictor has the form of a threshold model, composed of a scoring function and a set of thresholds, and our strategy is based on a reduction to fair binary classification for learning the scoring function and local search for choosing the thresholds. We provide generalization guarantees on the error and fairness violation of our predictor, and we illustrate the effectiveness of our approach in extensive experiments.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 23 of its 73 references that are in the map. First posted on arXiv 7 May 2021.

Builds on

23 of 73 references.

References · 23

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  3. 2018A Reductions Approach to Fair Classificationcited by 587
  4. 2014OpenML: networked science in machine learningcited by 526
  5. 2019Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classificationcited by 404
  6. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  7. 2018Fairness of Exposure in Rankingscited by 190
  8. 2018Fair Clustering Through Fairletscited by 176
  9. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  10. 2017A Convex Framework for Fair Regressioncited by 148
  11. 2019Rank consistent ordinal regression for neural networks with application to age estimationcited by 133
  12. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
11 more
  1. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  2. 2017FA*IR: A Fair Top-k Ranking Algorithmcited by 113
  3. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  4. 2017Ranking with Fairness Constraintscited by 78
  5. 2019Guarantees for Spectral Clustering with Fairness Constraintscited by 72
  6. 2018The Price of Fair PCA: One Extra Dimensioncited by 67
  7. 2019Fair k-Center Clustering for Data Summarizationcited by 65
  8. 2019Pairwise Fairness for Ranking and Regressioncited by 58
  9. 2019The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metriccited by 41
  10. 2018Fair and Diverse DPP-based Data Summarizationcited by 39
  11. 2018Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

6 papers.

  1. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  2. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
  3. 2022Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networkscited by 10
  4. 2022ORDSIM: Ordinal Regression for E-Commerce Query Similarity Predictioncited by 3
  5. 2023Linking convolutional kernel size to generalization bias in face analysis CNNscited by 1
  6. 2026Fairness Constraints in High-Dimensional Generalized Linear Modelscited by 0