FairRR: Pre-Processing for Group Fairness through Randomized Response
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Abstract (arXiv)
The increasing usage of machine learning models in consequential decision-making processes has spurred research into the fairness of these systems. While significant work has been done to study group fairness in the in-processing and post-processing setting, there has been little that theoretically connects these results to the pre-processing domain. This paper proposes that achieving group fairness in downstream models can be formulated as finding the optimal design matrix in which to modify a response variable in a Randomized Response framework. We show that measures of group fairness can be directly controlled for with optimal model utility, proposing a pre-processing algorithm called FairRR that yields excellent downstream model utility and fairness.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 52 references that are in the map. First posted on arXiv 12 Mar 2024.
Builds on
20 of 52 references.
References · 20
- 2016Equality of Opportunity in Supervised Learningcited by 2,000
- 2014Certifying and removing disparate impactcited by 715
- 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
- 2017Algorithmic decision making and the cost of fairnesscited by 402
- 2018Learning Adversarially Fair and Transferable Representationscited by 353
- 2015The Variational Fair Autoencodercited by 223
- 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
- 2016Fairness in Learning: Classic and Contextual Banditscited by 167
- 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
- 2020Fair Attribute Classification through Latent Space De-biasingcited by 125
- 2018The Measure and Mismeasure of Fairnesscited by 94
- 2020Learning Certified Individually Fair Representationscited by 69
8 more
- 2018iFair: Learning Individually Fair Data Representations for Algorithmic Decision Makingcited by 64
- 2019Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classificationcited by 60
- 2016A statistical framework for fair predictive algorithmscited by 37
- 2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
- 2021Classification with abstention but without disparitiescited by 27
- 2019Improved Adversarial Learning for Fair Classificationcited by 25
- 2022Fair Bayes-Optimal Classifiers Under Predictive Paritycited by 16
- 2024Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processingcited by 9
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.