Improving Fair Training under Correlation Shifts

• URL: http://arxiv.org/abs/2302.02323v1
• Date: Sun, 5 Feb 2023 07:23:35 GMT
• Title: Improving Fair Training under Correlation Shifts
• Authors: Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh
• Abstract summary: In particular, when the bias between labels and sensitive groups changes, the fairness of the trained model is directly influenced and can worsen. We analytically show that existing in-processing fair algorithms have fundamental limits in accuracy and group fairness. We propose a novel pre-processing step that samples the input data to reduce correlation shifts.
• Score: 33.385118640843416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model fairness is an essential element for Trustworthy AI. While many techniques for model fairness have been proposed, most of them assume that the training and deployment data distributions are identical, which is often not true in practice. In particular, when the bias between labels and sensitive groups changes, the fairness of the trained model is directly influenced and can worsen. We make two contributions for solving this problem. First, we analytically show that existing in-processing fair algorithms have fundamental limits in accuracy and group fairness. We introduce the notion of correlation shifts, which can explicitly capture the change of the above bias. Second, we propose a novel pre-processing step that samples the input data to reduce correlation shifts and thus enables the in-processing approaches to overcome their limitations. We formulate an optimization problem for adjusting the data ratio among labels and sensitive groups to reflect the shifted correlation. A key benefit of our approach lies in decoupling the roles of pre- and in-processing approaches: correlation adjustment via pre-processing and unfairness mitigation on the processed data via in-processing. Experiments show that our framework effectively improves existing in-processing fair algorithms w.r.t. accuracy and fairness, both on synthetic and real datasets.

Related papers

• Mitigating Matching Biases Through Score Calibration [1.5530839016602822]
  Biased outcomes in record matching can result in unequal error rates across demographic groups, raising ethical and legal concerns. In this paper, we adapt fairness metrics traditionally applied in regression models to evaluate cumulative bias across all thresholds in record matching. We propose a novel post-processing calibration method, leveraging optimal transport theory and Wasserstein barycenters, to balance matching scores across demographic groups.
  arXiv  Detail & Related papers  (2024-11-03T21:01:40Z)
• 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)
• Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach [72.19525160912943]
  We first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. We then analyze the sufficient conditions to guarantee fairness for the target dataset. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR)
  arXiv  Detail & Related papers  (2023-03-06T17:19:23Z)
• An Operational Perspective to Fairness Interventions: Where and How to Intervene [9.833760837977222]
  We present a holistic framework for evaluating and contextualizing fairness interventions. We demonstrate our framework with a case study on predictive parity. We find predictive parity is difficult to achieve without using group data.
  arXiv  Detail & Related papers  (2023-02-03T07:04:33Z)
• Domain Adaptation meets Individual Fairness. And they get along [48.95808607591299]
  We show that algorithmic fairness interventions can help machine learning models overcome distribution shifts. In particular, we show that enforcing suitable notions of individual fairness (IF) can improve the out-of-distribution accuracy of ML models.
  arXiv  Detail & Related papers  (2022-05-01T16:19:55Z)
• A Pre-processing Method for Fairness in Ranking [0.0]
  We propose a fair ranking framework that evaluates the order of training data in a pairwise manner. We show that our method outperforms the existing methods in the trade-off between accuracy and fairness over real-world datasets.
  arXiv  Detail & Related papers  (2021-10-29T02:55:32Z)
• Post-processing for Individual Fairness [23.570995756189266]
  Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. We consider a setting where the learner only has access to the predictions of the original model and a similarity graph between individuals, guiding the desired fairness constraints. Our algorithms correct individual biases in large-scale NLP models such as BERT, while preserving accuracy.
  arXiv  Detail & Related papers  (2021-10-26T15:51:48Z)
• Can Active Learning Preemptively Mitigate Fairness Issues? [66.84854430781097]
  dataset bias is one of the prevailing causes of unfairness in machine learning. We study whether models trained with uncertainty-based ALs are fairer in their decisions with respect to a protected class. We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
  arXiv  Detail & Related papers  (2021-04-14T14:20:22Z)
• 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)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)

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.