Repairing Regressors for Fair Binary Classification at Any Decision Threshold

• URL: http://arxiv.org/abs/2203.07490v4
• Date: Sun, 10 Dec 2023 20:08:43 GMT
• Title: Repairing Regressors for Fair Binary Classification at Any Decision Threshold
• Authors: Kweku Kwegyir-Aggrey, A. Feder Cooper, Jessica Dai, John Dickerson, Keegan Hines, Suresh Venkatasubramanian
• Abstract summary: We show that we can increase fair performance across all thresholds at once. We introduce a formal measure of Distributional Parity, which captures the degree of similarity in the distributions of classifications for different protected groups. Our main result is to put forward a novel post-processing algorithm based on optimal transport, which provably maximizes Distributional Parity.
• Score: 8.322348511450366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds. By decreasing the statistical distance between each group's score distributions, we show that we can increase fair performance across all thresholds at once, and that we can do so without a large decrease in accuracy. To this end, we introduce a formal measure of Distributional Parity, which captures the degree of similarity in the distributions of classifications for different protected groups. Our main result is to put forward a novel post-processing algorithm based on optimal transport, which provably maximizes Distributional Parity, thereby attaining common notions of group fairness like Equalized Odds or Equal Opportunity at all thresholds. We demonstrate on two fairness benchmarks that our technique works well empirically, while also outperforming and generalizing similar techniques from related work.

Related papers

• Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness [14.421493372559762]
  We quantify the impact of enforcing algorithmic fairness and group-blindness in binary classification under group fairness constraints. We propose a unified framework for fair classification that provides distribution-free and finite-sample fairness guarantees with controlled excess risk.
  arXiv  Detail & Related papers  (2024-10-21T20:04:17Z)
• Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing [32.5214395114507]
  We develop methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairness constraints. We show that several popular disparity measures -- the deviations from demographic parity, equality of opportunity, and predictive equality -- are bilinear. Our methods control disparity directly while achieving near-optimal fairness-accuracy tradeoffs.
  arXiv  Detail & Related papers  (2024-02-05T08:59:47Z)
• Boosting Fair Classifier Generalization through Adaptive Priority Reweighing [59.801444556074394]
  A performance-promising fair algorithm with better generalizability is needed. This paper proposes a novel adaptive reweighing method to eliminate the impact of the distribution shifts between training and test data on model generalizability.
  arXiv  Detail & Related papers  (2023-09-15T13:04:55Z)
• Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment [54.179859639868646]
  We propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories.
  arXiv  Detail & Related papers  (2023-07-27T07:42:44Z)
• Relational Proxies: Emergent Relationships as Fine-Grained Discriminators [52.17542855760418]
  We propose a novel approach that leverages information between the global and local part of an object for encoding its label. We design Proxies based on our theoretical findings and evaluate it on seven challenging fine-grained benchmark datasets. We also experimentally validate our theory and obtain consistent results across multiple benchmarks.
  arXiv  Detail & Related papers  (2022-10-05T11:08:04Z)
• How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts [107.72786199113183]
  We propose a novel fairness learning method termed CUrvature MAtching (CUMA) CUMA achieves robust fairness generalizable to unseen domains with unknown distributional shifts. We evaluate our method on three popular fairness datasets.
  arXiv  Detail & Related papers  (2022-07-04T02:37:50Z)
• Improved Approximation for Fair Correlation Clustering [4.629694186457133]
  Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing unfairness is a major challenge. Motivated by this, we study Fair Correlation Clustering where the data points may belong to different protected groups. Our paper significantly generalizes and improves on the quality guarantees of previous work of Ahmadi et al. and Ahmadian et al.
  arXiv  Detail & Related papers  (2022-06-09T03:07:57Z)
• Group-Aware Threshold Adaptation for Fair Classification [9.496524884855557]
  We introduce a novel post-processing method to optimize over multiple fairness constraints. Our method theoretically enables a better upper bound in near optimality than existing method under same condition. Experimental results demonstrate that our method outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-08T04:36:37Z)
• Beyond Individual and Group Fairness [90.4666341812857]
  We present a new data-driven model of fairness that is guided by the unfairness complaints received by the system. Our model supports multiple fairness criteria and takes into account their potential incompatibilities.
  arXiv  Detail & Related papers  (2020-08-21T14:14:44Z)
• Fairness with Overlapping Groups [15.154984899546333]
  A standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously. We reconsider this standard fair classification problem using a probabilistic population analysis. Our approach unifies a variety of existing group-fair classification methods and enables extensions to a wide range of non-decomposable multiclass performance metrics and fairness measures.
  arXiv  Detail & Related papers  (2020-06-24T05:01:10Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.